meaning speech synthesis

What Is Speech Synthesis And How Does It Work?

Curious about what is speech synthesis? Discover how this technology works and its various applications in this informative guide.

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Speech synthesis is the artificial production of human speech. This technology enables users to convert written text into spoken words. Text to speech technology can be a valuable tool for individuals with disabilities, language learners, educators, and more. In this blog, we will delve into the world of speech synthesis, exploring how it works, its applications, and its impact on various industries. Let's dive in and discover What is speech synthesis and how it is shaping the future of communication.

Table of Contents

What is speech synthesis, how does speech synthesis work, different approaches and techniques speech synthesizers use to produce audio waveforms, applications and use cases of speech synthesis, 7 best text to speech synthesizers on the market.

Text Analysis

This initial step involves contextual assimilation of the typed text. The software analyzes the text input to understand its context, including recognizing individual words, punctuation, and grammar. Text analysis helps the software generate accurate speech that reflects the intended meaning of the written content.

Linguistic Processing

Linguistic processing involves mapping the text to its corresponding unit of sound. This process helps convert the written words into phonetic sounds used to develop the spoken language. Linguistic processing ensures that the synthesized speech sounds natural and understandable to the listener.

Acoustic Processing

Acoustic processing plays a crucial role in generating the speech's sound qualities, such as pitch, intensity, and tempo. This step focuses on converting the linguistic representations into acoustic signals that mimic the qualities of human speech. Acoustic processing enhances the naturalness of the synthesized speech .

Audio Synthesis

The final step in the speech synthesis process involves the conversion of the generated sound in the textual sequence using synthetic voices or recorded human voices. Audio synthesis aims to create a realistic speech output that closely resembles human speech. This stage ensures that the synthesized speech is clear, coherent, and engaging for the listener.

Affordable Text-to-Speech Solution

If you are looking for cheap, scalable, realistic TTS to incorporate into your products, try our text-to-speech API for free today. Convert text into natural-sounding speech at an affordable and scalable price.

Text Input and Analysis

After entering the text you want to convert into speech, the TTS software analyzes the text to understand its linguistic components, breaking it down into phonemes, the smallest units of sound in a language. It then identifies punctuation, emphasis, and other cues to generate natural-sounding speech.

In this stage, the software applies rules of grammar and syntax to ensure that the speech sounds natural. It also incorporates intonation and prosody to convey meaning and emotion, enhancing the naturalness of the synthesized speech.

Linguistic information is converted into parameters governing speech sound generation, transforming linguistic features like phonemes and intonation into acoustic parameters. Pitch, duration, and amplitude are manipulated to produce speech sounds with the desired characteristics.

Acoustic parameters are combined to generate audible speech, possibly undergoing filtering and post-processing to enhance clarity and realism.

Accessible Text-to-Speech Technology

If you are looking for cheap, scalable, realistic TTS to incorporate into your products, try our text-to-speech API for free today. Convert text into enhanced clarity at an affordable and scalable price.

Concatenative Synthesis

Concatenative synthesis involves piecing together pre-recorded segments of speech to create the desired output. It relies on a database of recorded speech units, such as phonemes, syllables, or words, which are concatenated to form complete utterances. This approach can produce highly natural-sounding speech especially when the database contains a large variety of speech units.

Parametric Synthesis

Parametric synthesis generates speech signals by manipulating a set of acoustic parameters that represent various aspects of speech production. These parameters typically include fundamental frequency (pitch), formant frequencies, duration, and intensity. Rather than relying on recorded speech samples, parametric synthesis algorithms use mathematical models to generate speech sounds based on these parameters.

Articulatory Synthesis

Articulatory synthesis attempts to simulate the physical processes involved in speech production, modeling the movements of the articulatory organs (such as the tongue, lips, and vocal cords). It simulates the transfer function of the vocal tract to generate speech sounds based on articulatory gestures and acoustic properties. This approach aims to capture the underlying physiology of speech production, allowing for detailed control over articulatory features and acoustic output.

Formant Synthesis

Formant synthesis focuses on synthesizing speech by generating and manipulating specific spectral peaks, known as formants, which correspond to resonant frequencies in the vocal tract. By controlling the frequencies and amplitudes of these formants, formant synthesis algorithms can produce speech sounds with different vowel qualities and articulatory characteristics. This approach is particularly well-suited for synthesizing vowels and steady-state sounds, but it may struggle with accurately reproducing transient sounds and complex articulatory features.

Cutting-Edge Text-to-Speech Solution

Unreal Speech offers a low-cost, highly scalable text-to-speech API with natural-sounding AI voices which is the cheapest and most high-quality solution in the market. We cut your text-to-speech costs by up to 90%. Get human-like AI voices with our super fast / low latency API, with the option for per-word timestamps. With our simple easy-to-use API, you can give your LLM a voice with ease and offer this functionality at scale. If you are looking for cheap, scalable, realistic TTS to incorporate into your products, try our text-to-speech API for free today. Convert text into natural-sounding speech at an affordable and scalable price.

Speech synthesis technology has been a game-changer when it comes to making content more accessible for individuals with visual impairments. By using text-to-speech software, visually impaired individuals can now easily consume written content by listening to it. This eliminates the need for reading and allows them to have text read aloud to them directly from their devices. This innovation has opened up a world of opportunities for people with disabilities, enabling them to access information and tap into resources that were previously out of reach.

eLearning - Enhancing Educational Experiences with Voice Synthesizers

Voice synthesizers are revolutionizing the learning experience with the rise of eLearning platforms. Educators can now create interactive and engaging digital learning modules by leveraging speech synthesis technology.

By incorporating AI voices to read course content, voiceovers for videos, and audio elements, educators can create dynamic learning materials that enhance student engagement and bolster retention rates. This application of speech synthesis has proven to be instrumental in optimizing the learning process and fostering a more immersive educational environment.

Marketing and Advertising - Elevating Brand Communication Through Speech Synthesis

In the world of marketing, text-to-speech technology offers brands a powerful tool to enhance their communication strategies. By using synthetic voices that align with their brand identity, businesses can create voiceovers that resonate with their target audience.

Speech synthesis enables businesses to save costs that would otherwise be spent on hiring voice artists and audio engineers for advertising and promotional content. By integrating human-like voices into marketing videos and product demos, companies can effectively convey their brand message while saving on production expenses.

Content Creation - Crafting Engaging Multimedia Content with Speech Generation Tools

Another exciting application of speech generation technology is in the field of content creation. Content creators can now produce a wide range of multimedia content, including YouTube videos, audiobooks, podcasts, and more, using speech synthesis tools.

These tools enable creators to generate high-quality audio content that is engaging and captivating for their audience. By leveraging speech synthesis, content creators can explore new avenues of creativity and enhance the overall quality of their multimedia projects.

1. Unreal Speech: Cheap, Scalable, and Realistic TTS Synthesizer

Unreal Speech offers a low-cost, highly scalable text-to-speech API with natural-sounding AI voices, making it the cheapest and most high-quality solution in the market. It cuts your text-to-speech costs by up to 90%. With their super-fast API, you can get human-like AI voices with the option for per-word timestamps. The easy-to-use API allows you to give your LLM a voice effortlessly, offering this functionality at scale. If you are looking for cheap, scalable, and realistic TTS to incorporate into your products, Unreal Speech is the way to go.

2. Amazon Polly: Cloud-Based TTS Synthesizer

Amazon Polly's cloud-based TTS API uses Speech Synthesis Markup Language (SSML) to generate realistic speech from text. This enables users to integrate speech synthesis into applications seamlessly, enhancing accessibility and engagement.

3. Microsoft Azure: RESTful Architecture for TTS

Microsoft Azure's text-to-speech API follows a RESTful architecture for its text-to-speech interface. This cloud-based service supports flexible deployment, allowing users to run TTS at data sources.

4. Murf: Customizable High-Quality TTS Synthesizer

Murf is popular for its high-quality voiceovers and its ability to customize speech to a remarkable extent. It offers a unique voice model that delivers a lifelike user experience.

5. Speechify: Powerful TTS App Using AI

Speechify is a powerful text-to-speech app written in Python using artificial intelligence. It can help you convert any written text into natural-sounding speech.

6. IBM Watson Text to Speech: High-Quality, Natural-Sounding TTS

IBM Watson is known for its high-quality, natural-sounding voices. It provides a unique API that can be used in several programming languages, including Python.

7. Google Cloud Text to Speech: Global TTS Synthesizer

Google Cloud Text to Speech utilizes Google's powerful AI and machine learning capabilities to provide highly realistic voices. Supporting numerous languages and dialects, it is suitable for global enterprises.

Try Unreal Speech for Free Today — Affordably and Scalably Convert Text into Natural-Sounding Speech with Our Text-to-Speech API

Unreal Speech offers a cost-effective and scalable text-to-speech API with natural-sounding AI voices. It provides the cheapest and most high-quality solution in the market, reducing text-to-speech costs by up to 90%. With its super-fast/low latency API, Unreal Speech delivers human-like AI voices with the option for per-word timestamps. Its simple and easy-to-use API allows for giving your LLM a voice and offering this functionality at scale. If you are looking for an affordable, scalable, and realistic TTS solution to incorporate into your products, try Unreal Speech's text-to-speech API for free today to convert text into natural-sounding speech.

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Artwork: Humans don't communicate by printing words on their foreheads for other people to read, so why should computers? Thanks to smartphone agents like Siri, Cortana, and "OK Google," people are slowly getting used to the idea of speaking commands to a computer and getting back spoken replies.

What is speech synthesis?

How does speech synthesis work.

Artwork: Context matters: A speech synthesizer needs some understanding of what it's reading.

Artwork: Concatenative versus formant speech synthesis. Left: A concatenative synthesizer builds up speech from pre-stored fragments; the words it speaks are limited rearrangements of those sounds. Right: Like a music synthesizer, a formant synthesizer uses frequency generators to generate any kind of sound.

Articulatory

What are speech synthesizers used for.

Photo: Will humans still speak to one another in the future? All sorts of public announcements are now made by recorded or synthesized computer-controlled voices, but there are plenty of areas where even the smartest machines would fear to tread. Imagine a computer trying to commentate on a fast-moving sports event, such as a rodeo, for example. Even if it could watch and correctly interpret the action, and even if it had all the right words to speak, could it really convey the right kind of emotion? Photo by Carol M. Highsmith, courtesy of Gates Frontiers Fund Wyoming Collection within the Carol M. Highsmith Archive, Library of Congress , Prints and Photographs Division.

Who invented speech synthesis?

Artwork: Speak & Spell—An iconic, electronic toy from Texas Instruments that introduced a whole generation of children to speech synthesis in the late 1970s. It was built around the TI TMC0281 chip.

Anna (c. ~2005)

Olivia (c. ~2020).

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Current research, notes and references ↑    pre-processing in described in more detail in "chapter 7: speech synthesis from textual or conceptual input" of speech synthesis and recognition by wendy holmes, taylor & francis, 2002, p.93ff. ↑    for more on concatenative synthesis, see chapter 14 ("synthesis by concatenation and signal-process modification") of text-to-speech synthesis by paul taylor. cambridge university press, 2009, p.412ff. ↑    for a much more detailed explanation of the difference between formant, concatenative, and articulatory synthesis, see chapter 2 ("low-lever synthesizers: current status") of developments in speech synthesis by mark tatham, katherine morton, wiley, 2005, p.23–37. please do not copy our articles onto blogs and other websites articles from this website are registered at the us copyright office. copying or otherwise using registered works without permission, removing this or other copyright notices, and/or infringing related rights could make you liable to severe civil or criminal penalties. text copyright © chris woodford 2011, 2021. all rights reserved. full copyright notice and terms of use . follow us, rate this page, tell your friends, cite this page, more to explore on our website....

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speech synthesis

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• Columbia University - Department of Computer Science - Speech Synthesis
• Carnegie Mellon University - School of Computer Science - Speech Synthesis for educational technology
• National Center for Biotechnology Information - Models of speech synthesis

speech synthesis , generation of speech by artificial means, usually by computer . Production of sound to simulate human speech is referred to as low-level synthesis . High-level synthesis deals with the conversion of written text or symbols into an abstract representation of the desired acoustic signal, suitable for driving a low-level synthesis system. Among other applications, this technology provides speaking aid to the speech-impaired and reading aid to the sight-impaired.

Speech Synthesis: How It Works and Where to Get It

Ever wonder how Alexa reads you the weather every day? Learn the basics of speech synthesis—and meet the ReadSpeaker speech synthesis library.

Humans have been building talking machines for centuries—or at least trying to. Inventor Wolfgang von Kempelen nearly got there with bellows and tubes back in the 18th century. Bell Labs legend Homer Dudley succeeded in the 1930s. His “ Voder ” manipulated raw electronic sound to produce recognizable spoken language—but it required a highly trained operator and would have been useless for an iPhone.

When we talk about speech synthesis today, we usually mean one technology in particular: text to speech (TTS). This voice-modeling software translates written language into speech audio files, allowing Alexa to keep talking about new things. So how does speech synthesis work in the era of AI voice assistants and smart speakers?

A few technologies do the trick. One approach to TTS is called unit selection synthesis (USS). A USS engine sews chunks of recorded speech into new utterances. But in order to minimize audible pitch differences at the seams, the professional voice talent must record hours of speech in a fairly neutral and unvarying speaking style. As a result, USS voices sound less natural, and there is no flexibility to synthesize more expressive or emotional speaking styles without doubling or tripling the amount of recorded speech.

Instead, let’s look at neural text to speech, a form of speech synthesis AI that uses machine learning to produce more lifelike results. In this article, we’ll describe how neural TTS works.

Of course, USS voices are still being used in low-power applications like automotive audio systems. That might not last long, as scientists are continually reducing the computational power required by neural TTS. Soon, neural TTS may simply create all the synthetic voices you interact with—making it all the more important to understand.

Adding Speech Synthesis to Your Business Project

If you’re interested in speech synthesis, maybe you need TTS for a product, website, or project. Before we get into the details of how it works, you should know where to access TTS technology. That answer is simple: ReadSpeaker.

The ReadSpeaker speech synthesis library is an ever-growing collection of lifelike TTS solutions, all ready to deploy in your voicebot, smart speaker application, or voice user interface. Fill out the form below to start exploring the contents of our ready-made TTS voice portfolio for your organization’s needs. If you need more details first, read the last section of this article to learn what sets ReadSpeaker apart from the crowd.

Request TTS Voice Samples

Listen to ReadSpeaker’s neural TTS voices in dozens of languages and personas—or inquire about your brand’s very own custom voice. Start the conversation today!

Now that you know where to get your TTS, here are the basic steps a neural TTS engine uses to speak:

1. The TTS Engine Learns to Pronounce the Text

The first step in neural speech synthesis is called linguistic pre-processing , in which the TTS software converts written language into a detailed pronunciation guide.

First and foremost, the TTS engine needs to understand how to pronounce the text. That requires translation into a phonetic transcription, a pronunciation guide with words represented as phonemes. (Phonemes are the building blocks of spoken words. For instance, “cat” is made up of three phonemes: the /k/ sound represented by the letter “c,” the short vowel /a/ represented by the letter “a,” and the plosive /t/ at the end.)

The TTS engine matches combinations of letters to corresponding phonemes to build this phonetic transcription. The system also consults pre-programmed rules. These rules are especially important for numerals and dates—the system needs to decide whether “1920” means “one thousand, nine hundred and twenty” or “nineteen-twenty” before it can break the text down into its constituent parts, for instance.

In addition to phonemes, the TTS engine identifies stresses: syllables with a slightly raised pitch, some extra volume, and/or an incrementally longer duration, like the “but” in “butter.” At the end of linguistic pre-processing, the text represents a string of stressed and unstressed phonemes. That’s the input for the neural networks to come.

2. A DNN Translates Text Into Numbers

Next comes sequence-to-sequence processing, in which a deep neural network (DNN) translates text into numbers that represent sound.

The sequence-to-sequence network is software that translates your prepared script into a two-dimensional mathematical model of sound called a spectrogram. At its simplest, a spectrogram is a Cartesian plane in which the X axis represents time and the Y axis represents frequency.

The system generates these spectrograms by consulting training data. The neural network has already processed recordings of a human speaker. It has broken down those recordings into phoneme models (plus lots of other parts, but let’s keep this simple). So it has an idea of what the spectrograms for a given speaker look like. When it encounters a new text, the network maps each speech element to a training-derived spectrogram.

Long story short: The sequence-to-sequence network matches phonetic transcriptions to spectrogram representations inferred from the original training data.

What does the spectrogram do?

The spectrogram contains numerical values for each frame, or a temporal snapshot, of the represented sound—and the TTS engine needs these numbers to build a voice audio file. Essentially, the sequence-to-sequence model maps text onto spectrograms, which translate text into numbers. Those numbers represent the precise acoustic characteristics of whoever’s voice was in the training data recordings, if that speaker were to say the words represented in the phonetic transcription.

The Role of Generative Neural Networks in Neural TTS

You’ve probably heard of generative AI models, like ChatGPT. Increasingly, we use similar types of generative neural networks to create lifelike synthetic voices.

A generative neural network creates novel speech samples in a random but controllable way. Such a model leads to more natural speech waveforms, especially when applied at the vocoder stage (see below).

Not all DNNs are generative, of course. Generative neural networks are just the latest in a rapidly developing series of AI technologies—and we use several of them to create neural TTS voices.

3. A Vocoder Produces Waveforms You Can Listen To

The final step in neural speech synthesis is waveform production , in which the spectrogram is converted into a playable audio medium: a waveform that is playable or streamable. These waveforms can be stored as audio files . That makes the completed neural TTS voice available in audio file production systems or as real-time streaming audio.

But first, we must convert the spectrogram into the speech waveform.

We’ve translated text into phonemes and phonemes into spectrograms and spectrograms into numbers: How do you turn those numbers into sound? The answer is another type of neural network called a vocoder . Its job is to translate the numerical data from the spectrogram into a playable audio file.

The vocoder requires training from the same audio data you used to create the sequence-to-sequence model. That training data provides information that the vocoder uses to predict the best mapping of spectrogram data onto a digital audio sample. Once the vocoder has performed its translation, the system gives you its final output: An audio file, synthesized speech in its consumable form.

That’s a highly simplified picture of how speech synthesis works, of course. Dig deeper by learning how ReadSpeaker creates bespoke neural voices for brands and application creators. There’s one more question we should address, though:

With dozens of TTS providers just a Google search away, why should you choose ReadSpeaker? Here are eight good reasons.

Why choose TTS voices from the ReadSpeaker speech synthesis library?

1. every voice offers accurate pronunciation, lifelike expression, and ai-driven quality..

Text-to-speech voice quality is essential for providing outstanding customer experiences in voice-first environments. ReadSpeaker has been at the forefront of machine speech for more than 20 years, and we continually invest in R&D to push the technology forward. Our DNN technology allows us to synthesize human voices with remarkable—and constantly improving—accuracy.

As a result, the voices in our speech synthesis library sound as good as TTS can—and they always will, regardless of how the technology advances. As we develop new neural networks for modeling lifelike human speech, we’ll reprocess original source recordings to keep our TTS voices future-proof. This makes ReadSpeaker text to speech a unique solution.

2. The ReadSpeaker team offers personalized, ongoing customer support.

As a company solely focused on TTS, ReadSpeaker has a dedicated customer support team. We help you choose the right TTS product; we help you integrate it with your systems; and we remain available to resolve any issues that arise for the full duration of your TTS journey. Most providers of TTS simply sell a tech product, then leave you on your own. At ReadSpeaker, we partner with you over the long term to ensure success.

3. Custom pronunciation dictionaries ensure accurate speech.

The most advanced TTS engines in the world still run into some pronunciation problems. Acronyms, proper nouns, and industry jargon can throw them for a loop—and lead to inaccurate speech, adding to the user’s confusion instead of removing it.

At ReadSpeaker, we offer personalized pronunciation dictionaries. While our linguists work to ensure accurate speech from the start, your custom dictionary covers the unique language involved in your use case. That could be obscure scientific terms, niche industry buzzwords, names of people and places, or anything else. Customization ensures accurate speech, and ReadSpeaker’s pronunciation dictionaries put you in control.

4. ReadSpeaker provides global reach with a local touch.

ReadSpeaker TTS voices are available in more than 35 languages and dialects, allowing you to reach a global audience while serving distinct communities—whether they speak the Dutch of Belgium or the Netherlands; Australian, British, or U.S. English; Mandarin or Cantonese; or many other languages and dialects. Our list is always growing to meet the needs of new customers.

Don’t see your customers’ language represented? Contact us to discuss your project’s language requirements. Meanwhile, with offices in over 10 countries, ReadSpeaker linguists are always close at hand to solve pronunciation challenges including industry- and brand-specific jargon.

5. ReadSpeaker is 100% focused on voice technology.

Many major providers of TTS voices do so as an adjunct to professional services; they create conversational AI solutions, devoting the bulk of their R&D efforts to related technologies like natural language understanding (NLU) or conversation management systems rather than TTS.

At ReadSpeaker, TTS is all we do—and all of our R&D concentrates on improving synthetic speech. This deep, narrow focus also gives us the flexibility to work hand-in-hand with customers, ensuring expectation-defying TTS experiences before, during, and after launch. As part of our continuous improvement policy, we take feedback from our users, constantly update our products, and maintain an international team of computational linguists who will help you update custom pronunciation dictionaries. This ensures ongoing perfect speech, even for changing industry jargon, acronyms, initialisms, and proper nouns—the very specifics other TTS engines struggle to express accurately.

6. Get TTS for all your voice channels in one place.

What are your TTS goals? You might need to dynamically voice-enable your website, voicebot, or device; produce video voiceovers; integrate voice into a learning management system (LMS); or embed runtime speech into an application or a video game. Maybe you need all of these options and then some. Many TTS providers can only help with one or two of these technologies. ReadSpeaker provides solutions for every voice scenario.

By choosing a single TTS provider for all your voice touchpoints, you’ll cut down on costs and vendor management challenges. And if you create a custom TTS voice, ReadSpeaker allows you to provide brand consistency everywhere you meet your customers: Voice ads, automated customer experiences, brand identity, interactive voice response (IVR) systems, owned voice assistants, and more.

7. Our TTS engines ensure full privacy, every time and for everyone.

Currently, some of the leading providers of ReadSpeaker-quality TTS voices are Big Tech giants. Often, conversational AI providers simply rely on these industry behemoths to supply their speech synthesis libraries. That can create potential conflicts of interest, as vendors may access and analyze user data.

That’s not a risk with ReadSpeaker, and the reason is simple: ReadSpeaker TTS solutions never collect data, not from our customers, and certainly not from yours. As an added bonus, this assured privacy can help you comply with local regulatory laws, whether that’s GDPR in the EU, HIPAA in the U.S., or any other privacy protection.

8. Choose licensing based on your business model, not ours.

Many TTS providers stick to rigid contracts, often with hefty minimum purchase volumes. At ReadSpeaker, we’ll work with you to create a contract that reflects your business model, whether that’s licensing the perfect voice for a certain duration or partnering with you to meet other pre-agreed goals.

For even greater branding gains, ask us about our custom branded voices —a one-of-a-kind TTS voice built to express your brand traits and establish you as a distinct voice in all your consumer-outreach channels.

Sound interesting? Explore the ReadSpeaker speech synthesis library to find your language and listen to TTS voice samples. Better yet, contact us to request a curated selection of neural TTS voices for your unique application—or to develop a custom voice as an audio brand signature.

ReadSpeaker’s industry-leading voice expertise leveraged by leading Italian newspaper to enhance the reader experience Milan, Italy. – 19 October, 2023 – ReadSpeaker, the most trusted,…

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The Ultimate Guide to Speech Synthesis in 2024

We've reached a stage where technology can mimic human speech with such precision that it's almost indistinguishable from the real thing. Speech synthesis, the process of artificially generating speech, has advanced by leaps and bounds in recent years, blurring the lines between what's real and what's artificially created. In this blog, we'll delve into the fascinating world of speech synthesis, exploring its history, how it works, and what the future holds for this cutting-edge technology. You can see speech sythesis in action with Murf studio for free.

Table of Contents

What is speech synthesis, text to written words, words to phonemes, concatenative, articulatory, assistive technology, marketing and advertising, content creation, software that use speech synthesis, why is murf the best speech synthesis software, what is speech synthesis, why is speech synthesis important, where can i use speech synthesis, what is the best speech synthesis software.

Speech synthesis, in essence, is the artificial simulation of human speech by a computer or any advanced software. It's more commonly also called text to speech . It is a three-step process that involves:

Contextual assimilation of the typed text

Mapping the text to its corresponding unit of sound

Generating the mapped sound in the textual sequence by using synthetic voices or recorded human voices

The quality of the human speech generated depends on how well the software understands the textual context and converts it into a voice.

Today, there is a multitude of options when it comes to text to speech software. They all provide different (and sometimes unique) features that help enhance the quality of synthesized speech. 

Speech generation finds extensive applications in assistive technologies, eLearning, marketing, navigation, hands-free tech, and more. It helps businesses with the cost-optimization of their marketing campaigns and assists those with vision impairments to 'read' text by hearing it read aloud, among other things. Let's understand how this technology works in more detail.

How Does Speech Synthesis Work?

The process of voice synthesis is quite interesting. Speech synthesis is done in three simple steps:

Text-to-word conversion

Word-to-phoneme conversion

Phoneme-to-sound conversion

Text to audio conversion happens within seconds, depending on the accuracy and efficiency of the software in use. Let's understand this process.

Before input text can be completely converted into intelligible human speech,   voice synthesizers must first polish and 'clean up' the entered text. This process is called 'pre-processing' or 'normalization.'

Normalization helps the TTS systems understand the context in which a text needs to be converted into synthesized speech. Without normalization, the converted speech likely ends up sounding unnatural or like complete gibberish.

To understand better, consider the case of abbreviations: "St." is read as "Saint." Without normalization, the software would just read it according to the phonetic rules instead of contextual insight. This may lead to errors.

The second step in text to speech conversion is working with the normalized text and locating the phonemes for each one. Every TTS software has a library of phonemes that corresponds to specific written words. A phoneme is a unique unit of sound that is attributed to a particular word in a language. It helps the text to speech software distinguish one word from another in any language.

When the software receives normalized input, it immediately begins locating the respective phonemes and pieces together bits of sound. However, there's one more catch involved: not all the words that are written the same are read the same way. So, the software looks up the context of the entire sentence to determine the most suitable prosody for a word and selects the right phonemes for output.

For example, "lead" can be read in two ways—"ledd" and "leed." The software selects the most suitable phoneme depending on the context in which the sentence is written.

Phonemes to Sounds

The final step is converting phonemes to sounds. While phonemes determine which sound goes with which word, the software is yet to  produce  any sound at all. There are three ways that the software produces audio waveforms:

This is the method where the software uses pre-recorded bits of the human voice for output. The software works by understanding the recorded snippets and rearranging them according to the list of phonemes it created as the output speech.

The formant method is similar to the way any other electronic device generates sound. By mimicking the frequency, wavelengths, pitches, and other properties of the phonemes in the generated list, the software can generate its own sound. This method is more effective than the concatenative one.

This is the most complex kind of custom speech synthesizer chip that exists (aside from the natural human voicebox) and is capable of mimicking human voice in surprising closeness.

Applications of Speech Synthesis

Speech generation isn't just made for individuals or businesses: it's a noble and inclusive technology that has generated a positive wave across the world by allowing the masses to 'read' by 'listening.' Some of the most notable speech synthesis applications are:

One of the most beneficial speech generation applications   is in assistive technology. According to data from WHO , there are about 2.2 billion people with some form of vision impairment worldwide. That's a lot of people, considering how important reading is for personal development and betterment.

With text to speech software, it has now become possible for these masses to consume typed content by listening to it. Text to speech eliminates the need for reading for visually-impaired people altogether. They can simply listen to the text on the screen or scan a piece of text onto their mobile devices and have it read aloud to them.

eLearning has been on a constant rise since the pandemic restricted most of the world's population to their homes. Today, people have realized how convenient it is to learn new concepts through eLearning videos and explainer videos .

Educators use voice synthesizers to create digital learning modules for learners, enabling a more immersive and engaging learning experience and environment for them. This catalysis has proved to be elemental in improving cognition and retention amongst students.

eLearning courses use speech synthesizers in the following ways:

Deploy AI voices to read the course content out loud

Create voiceovers for video and audio

Create learning prompts

Marketing and advertising are niches that require careful branding and representation. Text to speech gives brands the flexibility to create voiceovers in voices that represent their brand perfectly.

Additionally, speech synthesis helps businesses save a lot of money as well. By adding synthetic, human-like voices to their advertising videos and product demos , businesses save the expenses required for hiring and paying:

Audio engineers

Voice artists

AI voices also help save time while editing the script, eliminating the need to re-record an artist's voice with a new script. The text to speech tool can work with the text to produce audio through the edited script.

One of the most interesting applications of speech generation tools is the creation of video and audio content that is highly engaging. For example, you can create YouTube videos ,  audiobooks ,  podcasts,  and even lyrical tracks using these tools.

Without investing in voice artists, you can leverage hundreds of AI voices and edit them to your preferences. Many TTS tools allow you to adjust:

The pitch of the AI voice

Reading speed

This enables content creators to tailor AI voices to the needs and nature of their content and make it more impactful and engaging.

Natural Readers

Well Said Labs

Amazon Polly

When it comes to TTS, the two most important factors are the quality of output and its brand fit. These are the aspects that Murf helps your business get right with its text to speech modules that have customization capabilities second to none.

Some of the key features and capabilities of the Murf platform are:

Voice editing with adjustments to pitch, volume, emphasis, intonation, pause, speed, and emotion

Voice cloning feature for enterprises that allows them to create a custom voice that is an exact clone of their brand voice for any commercial requirement. 

Voice changer that lets you convert your own recorded voice to a professional sounding studio quality voiceover

Wrapping Up

If you've found yourself needing a voiceover for whichever purpose, text to speech (or speech generation) is your ideal solution. Thankfully, Murf covers all the bases while delivering exemplary performance, customizability, high quality, and variety in text to speech, which makes this platform one of the best in the industry. To generate speech samples for free, visit Murf today.

Speech synthesis is the technology that generates spoken language as output by working with written text as input. In other words, generating text from speech is called speech synthesis. Today, many software offer this functionality with varying levels of accuracy and editability.

Speech generation has become an integral part of countless activities today because of the convenience and advantages it provides. It's important because:

It helps businesses save time and money.

It helps people with reading difficulties understand text.

It helps make content more accessible.

Speech synthesis can be used across a variety of applications:

To create audiobooks and other learning media

In read-aloud applications to help people with reading, vision, and learning difficulties

In hands-free technologies like GPS navigation or mobile phones

On websites for translations or to deliver the key information audibly for better effect

…and many more.

Murf AI is the best TTS software because it allows you to hyper-customize your AI voices and mold them according to your voiceover needs. It also provides you with a suite of tools to further purpose your AI voices for applications like podcasts, audiobooks, videos, audio, and more.

You should also read:

How to create engaging videos using TikTok text to speech

An in-depth Guide on How to Use Text to Speech on Discord

Medical Text to Speech: Changing Healthcare for the Better

Get Text to Speech

Text to Speech News

What is Speech Synthesis

• Category: TTS Tech

Table of Contents

Speech synthesis: get familiar with it..

Speech synthesis, a marvel of technology, transforms written text into human-like speech. This process, often referred to as text-to-speech (TTS), uses algorithms and neural networks to generate synthesized speech that mirrors natural human voice. From its inception, speech synthesis systems have revolutionized the way we interact with machines and have found applications in various sectors including assistive technology, content creation, and telecommunications.

The Early Days: From Bell Laboratories to Voder

The journey of speech synthesis began in the 1930s at Bell Laboratories, with the creation of the Voder (Voice Operating Demonstrator) – the first speech synthesizer. This device laid the groundwork for subsequent developments in the field. It was designed to replicate human speech by manipulating sound waveforms.

The Evolution of Speech Synthesizers

Over the decades, speech synthesizers have evolved significantly. Early systems relied on formant synthesis, mimicking the human vocal tract to produce speech sounds. The 1970s and 1980s witnessed a shift towards concatenative synthesis, where pre-recorded speech units (phonemes) were stitched together to create speech.

Breakthroughs in TTS Systems

The introduction of TTS systems brought a significant leap in speech synthesis. These systems converted written text, including abbreviations and homographs, into natural-sounding speech. The normalization process, a part of TTS, ensures that the text is in a suitable format for conversion, handling the transcription of numbers, dates, and other special forms of written words.

The Role of AI and Neural Networks

The advent of artificial intelligence (AI) and neural networks in recent years has led to the development of high-quality, real-time speech synthesis systems. These systems, such as Microsoft’s Cortana, Amazon’s Alexa, and Apple’s Siri, have become a part of everyday life. They employ complex algorithms to generate voice output that closely resembles human speech, including its prosody and articulatory features.

Text-to-Speech Synthesis in Different Languages

English, being a widely spoken language, has seen significant advancements in text-to-speech synthesis. However, the technology has also made strides in other languages, adapting to different phonetic structures and speech sounds.

The Impact of TTS in Assistive Technology

One of the most profound impacts of TTS technology is in the realm of assistive technology. It has enabled people with disabilities to access written content through synthesized speech. TTS systems have become a voice for those who need them, offering new avenues of independence and communication.

Speech Synthesis in Content Creation and Media

The versatility of speech synthesis is evident in its use in content creation. From voiceovers in video games to podcasts, TTS systems offer a range of synthetic voices, including the option of a female voice, enhancing the diversity and inclusivity of content.

Speech Recognition: The Other Side of the Coin

Speech recognition, a technology closely related to speech synthesis, has evolved in tandem. It involves the conversion of spoken words into written text, a reverse process of TTS. Together, these technologies have transformed various sectors, including GPS navigation and telecommunications.

The Future: Towards More Natural Voices

The future of speech synthesis holds immense promise. With advancements in natural language processing and vocal tract modeling, TTS systems are moving towards creating even more natural-sounding voices. The focus is on improving the prosody and emotional expressiveness of synthetic speech.

A World Reshaped by Speech Synthesis

As we look ahead, speech synthesis systems, powered by neural networks and AI, are set to further blur the lines between human and machine interaction. The potential applications are vast, from assistive technology to new forms of content creation. The journey from the Voder to today’s sophisticated TTS systems exemplifies a remarkable technological evolution, one that continues to shape our world in profound ways.

Posted by Skyler Lee

Skyler is a passionate tech blogger and digital enthusiast known for her insightful and engaging content. With a background in computer science and over a decade of experience in the tech industry, Skyler has a deep understanding of technology trends and innovations. She launched her blog, "Get Text to Speech," as a platform to share her knowledge and excitement about everything TTS & AI.

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Speech Synthesis

Speech synthesis is the process of generating human-like speech from text, playing a crucial role in human-computer interaction. This article explores the advancements, challenges, and practical applications of speech synthesis technology.

Speech synthesis has evolved significantly in recent years, with researchers focusing on improving the naturalness, emotion, and speaker identity of synthesized speech. One such development is the Multi-task Anthropomorphic Speech Synthesis Framework (MASS), which can generate speech with specified emotion and speaker identity. This framework consists of a base Text-to-Speech (TTS) module and two voice conversion modules, enabling more realistic and versatile speech synthesis.

Recent research has also investigated the use of synthesized speech as a form of data augmentation for low-resource speech recognition. By experimenting with different types of synthesizers, researchers have identified new directions for future research in this area. Additionally, studies have explored the incorporation of linguistic knowledge to visualize and evaluate synthetic speech model training, such as analyzing vowel spaces to understand how a model learns the characteristics of a specific language or accent.

Some practical applications of speech synthesis include:

1. Personalized spontaneous speech synthesis: This approach focuses on cloning an individual's voice timbre and speech disfluency, such as filled pauses, to create more human-like and spontaneous synthesized speech.

2. Articulation-to-speech synthesis: This method synthesizes speech from the movement of articulatory organs, with potential applications in Silent Speech Interfaces (SSIs).

3. Data augmentation for speech recognition: Synthesized speech can be used to enhance the training data for speech recognition systems, improving their performance in various domains.

A company case study in this field is WaveCycleGAN2, which aims to bridge the gap between natural and synthesized speech waveforms. The company has developed a method that alleviates aliasing issues in processed speech waveforms, resulting in higher quality speech synthesis.

In conclusion, speech synthesis technology has made significant strides in recent years, with researchers focusing on improving the naturalness, emotion, and speaker identity of synthesized speech. By incorporating linguistic knowledge and exploring new applications, speech synthesis has the potential to revolutionize human-computer interaction and enhance various industries.

What is speech synthesis?

Speech synthesis is the process of generating human-like speech from text, which plays a crucial role in human-computer interaction. It involves converting written text into spoken words using algorithms and techniques that mimic the natural patterns, intonation, and rhythm of human speech. The goal of speech synthesis is to create a more seamless and intuitive communication experience between humans and computers.

What is an example of speech synthesis?

An example of speech synthesis is the text-to-speech (TTS) feature found in many devices and applications, such as smartphones, e-readers, and virtual assistants like Amazon Alexa or Google Assistant. These systems use speech synthesis technology to convert written text into spoken words, allowing users to listen to content instead of reading it, or to interact with devices using voice commands.

How is speech synthesis done?

Speech synthesis is typically done using a combination of algorithms and techniques that analyze the input text, break it down into smaller units (such as phonemes or syllables), and then generate the corresponding speech sounds. There are two main approaches to speech synthesis: concatenative synthesis and parametric synthesis. Concatenative synthesis involves assembling pre-recorded speech segments to create the final output. This method can produce high-quality, natural-sounding speech but requires a large database of recorded speech samples. Parametric synthesis, on the other hand, uses mathematical models to generate speech waveforms based on the input text's linguistic and acoustic features. This approach is more flexible and requires less storage, but the resulting speech may sound less natural compared to concatenative synthesis. Recent advancements in speech synthesis, such as deep learning-based methods, have led to significant improvements in the naturalness and quality of synthesized speech.

What are the practical applications of speech synthesis?

Some practical applications of speech synthesis include: 1. Text-to-speech (TTS) systems: These systems convert written text into spoken words, enabling users to listen to content or interact with devices using voice commands. 2. Personalized spontaneous speech synthesis: This approach focuses on cloning an individual's voice timbre and speech disfluency, such as filled pauses, to create more human-like and spontaneous synthesized speech. 3. Articulation-to-speech synthesis: This method synthesizes speech from the movement of articulatory organs, with potential applications in Silent Speech Interfaces (SSIs). 4. Data augmentation for speech recognition: Synthesized speech can be used to enhance the training data for speech recognition systems, improving their performance in various domains.

What are the current challenges in speech synthesis?

Current challenges in speech synthesis include: 1. Naturalness: Achieving a high level of naturalness in synthesized speech remains a challenge, as it requires capturing the subtle nuances, intonation, and rhythm of human speech. 2. Emotion and speaker identity: Generating synthesized speech with specific emotions or speaker identities is a complex task, as it involves modeling the unique characteristics of individual voices and emotional expressions. 3. Low-resource languages: Developing speech synthesis systems for low-resource languages can be difficult due to the limited availability of high-quality training data. 4. Integration with other technologies: Combining speech synthesis with other technologies, such as speech recognition or natural language processing, can be challenging, as it requires seamless interaction between different components and algorithms. By addressing these challenges, researchers and developers can continue to advance speech synthesis technology and expand its potential applications.

Speech Synthesis Further Reading

Explore more machine learning terms & concepts.

Speech recognition technology enables machines to understand and transcribe human speech, paving the way for applications in various fields such as military, healthcare, and personal assistance. This article explores the advancements, challenges, and practical applications of speech recognition systems. Speech recognition systems have evolved over the years, with recent developments focusing on enhancing their performance in noisy conditions and adapting to different accents. One approach to improve performance is through speech enhancement, which involves processing speech signals to reduce noise and improve recognition accuracy. Another approach is to use data augmentation techniques, such as generating synthesized speech, to train more robust models. Recent research in the field of speech recognition has explored various aspects, such as: 1. Evaluating the effectiveness of Gammatone Frequency Cepstral Coefficients (GFCCs) compared to Mel Frequency Cepstral Coefficients (MFCCs) for emotion recognition in speech. 2. Investigating the feasibility of using synthesized speech for training speech recognition models and improving their performance. 3. Studying the impact of non-speech sounds, such as laughter, on speaker recognition systems. These studies have shown promising results, with GFCCs outperforming MFCCs in speech emotion recognition and the inclusion of non-speech sounds during training improving speaker recognition performance. Practical applications of speech recognition technology include: 1. Speech-driven text retrieval: Integrating speech recognition with text retrieval methods to enable users to search for information using spoken queries. 2. Emotion recognition: Analyzing speech signals to identify the emotional state of the speaker, which can be useful in customer service, mental health, and entertainment industries. 3. Assistive technologies: Developing tools for people with disabilities, such as speech-to-text systems for individuals with hearing impairments or voice-controlled devices for those with mobility limitations. A company case study in this field is Mozilla's Deep Speech, an end-to-end speech recognition system based on deep learning. The system is trained using Recurrent Neural Networks (RNNs) and multiple GPUs, primarily on American-English accent datasets. By employing transfer learning and data augmentation techniques, researchers have adapted Deep Speech to recognize Indian-English accents, demonstrating the potential for the system to generalize to other English accents. In conclusion, speech recognition technology has made significant strides in recent years, with advancements in machine learning and deep learning techniques driving improvements in performance and adaptability. As research continues to address current challenges and explore new applications, speech recognition systems will become increasingly integral to our daily lives, enabling seamless human-machine interaction.

SqueezeNet: A compact deep learning architecture for efficient deployment on edge devices. SqueezeNet is a small deep neural network (DNN) architecture that achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters and less than 0.5MB model size. This compact architecture offers several advantages, including reduced communication during distributed training, lower bandwidth requirements for model deployment, and feasibility for deployment on hardware with limited memory, such as FPGAs. The development of SqueezeNet was motivated by the need for efficient DNN architectures suitable for edge devices, such as mobile phones and autonomous cars. By reducing the model size and computational requirements, SqueezeNet enables real-time applications and lower energy consumption. Several studies have explored modifications and extensions of the SqueezeNet architecture, resulting in even smaller and more efficient models, such as SquishedNets and NU-LiteNet. Recent research has focused on combining SqueezeNet with other machine learning algorithms and techniques, such as wavelet transforms and multi-label classification, to improve performance in various applications, including drone detection, landmark recognition, and industrial IoT. Additionally, SqueezeJet, an FPGA accelerator for the inference phase of SqueezeNet, has been developed to further enhance the speed and efficiency of the architecture. In summary, SqueezeNet is a compact and efficient deep learning architecture that enables the deployment of DNNs on edge devices with limited resources. Its small size and low computational requirements make it an attractive option for a wide range of applications, from object recognition to industrial IoT. As research continues to explore and refine the SqueezeNet architecture, we can expect even more efficient and powerful models to emerge, further expanding the potential of deep learning on edge devices.

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In This Article Expand or collapse the "in this article" section Speech Synthesis

Introduction, textbooks, edited collections, surveys, and introductions.

• Journals and Conferences
• Formant Synthesis
• Concatenative Synthesis Based on Diphones
• Text Processing, Pronunciation Dictionaries, and Letter-to-Sound
• Fundamental Frequency Estimation
• Representing Prosody
• Predicting Prosody
• Mainstream Unit Selection
• Trainable Unit Selection and Hybrid Methods
• Source-Filter Signal Processing: Linear Prediction
• Abstracting away from the Vocal Tract Filter: The Spectral Envelope
• Avoiding Explicit Source-Filter Separation
• Statistical Parametric Speech Synthesis
• Subjective Tests
• Objective Measures

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Speech Synthesis by Simon King LAST REVIEWED: 25 February 2016 LAST MODIFIED: 25 February 2016 DOI: 10.1093/obo/9780199772810-0024

Speech synthesis has a long history, going back to early attempts to generate speech- or singing-like sounds from musical instruments. But in the modern age, the field has been driven by one key application: Text-to-Speech (TTS), which means generating speech from text input. Almost universally, this complex problem is divided into two parts. The first problem is the linguistic processing of the text, and this happens in the front end of the system. The problem is hard because text clearly does not contain all the information necessary for reading out loud. So, just as human talkers use their knowledge and experience when reading out loud, machines must also bring additional information to bear on the problem; examples include rules regarding how to expand abbreviations into standard words, or a pronunciation dictionary that converts spelled forms into spoken forms. Many of the techniques currently used for this part of the problem were developed in the 1990s and have only advanced very slowly since then. In general, techniques used in the front end are designed to be applicable to almost any language, although the exact rules or model parameters will depend on the language in question. The output of the front end is a linguistic specification that contains information such as the phoneme sequence and the positions of prosodic phrase breaks. In contrast, the second part of the problem, which is to take the linguistic specification and generate a corresponding synthetic speech waveform, has received a great deal of attention and is where almost all of the exciting work has happened since around 2000. There is far more recent material available on the waveform generation part of the text-to-speech problem than there is on the text processing part. There are two main paradigms currently in use for waveform generation, both of which apply to any language. In concatenative synthesis, small snippets of prerecorded speech are carefully chosen from an inventory and rearranged to construct novel utterances. In statistical parametric synthesis, the waveform is converted into two sets of speech parameters: one set captures the vocal tract frequency response (or spectral envelope) and the other set represents the sound source, such as the fundamental frequency and the amount of aperiodic energy. Statistical models are learned from annotated training data and can then be used to generate the speech parameters for novel utterances, given the linguistic specification from the front end. A vocoder is used to convert those speech parameters back to an audible speech waveform.

Steady progress in synthesis since around 1990, and the especially rapid progress in the early 21st century, is a challenge for textbooks. Taylor 2009 provides the most up-to-date entry point to this field and is an excellent starting point for students at all levels. For a wider-ranging textbook that also provides coverage of Natural Language Processing and Automatic Speech Recognition, Jurafsky and Martin 2009 is also excellent. For those without an electrical engineering background, the chapter by Ellis giving “An Introduction to Signal Processing for Speech” in Hardcastle, et al. 2010 is essential background reading, since most other texts are aimed at readers with some previous knowledge of signal processing. Most of the advances in the field since around 2000 have been in the statistical parametric paradigm. No current textbook covers this subject in sufficient depth. King 2011 gives a short and simple introduction to some of the main concepts, and Taylor 2009 contains one relatively brief chapter. For more technical depth, it is necessary to venture beyond textbooks, and the tutorial article Tokuda, et al. 2013 is the best place to start, followed by the more technical article Zen, et al. 2009 . Some older books, such as Dutoit 1997 , still contain relevant material, especially in their treatment of the text processing part of the problem. Sproat’s comment that “text-analysis has not received anything like half the attention of the synthesis community” (p. 73) in his introduction to text processing in van Santen, et al. 1997 is still true, and Yarowsky’s chapter on homograph disambiguation in the same volume still represents a standard solution to that particular problem. Similarly, the modular system architecture described by Sproat and Olive in that volume is still the standard way of configuring a text-to-speech system.

Dutoit, Thierry. 1997. An introduction to text-to-speech synthesis . Norwell, MA: Kluwer Academic.

DOI: 10.1007/978-94-011-5730-8

Starting to get dated, but still contains useful material.

Hardcastle, W. J., J. Laver, and F. E. Gibbon. 2010. The handbook of phonetic sciences . Blackwell Handbooks in Linguistics. Oxford: Wiley-Blackwell.

DOI: 10.1002/9781444317251

A wealth of information, one highlight being the excellent chapter by Ellis introducing speech signal processing to readers with minimal technical background. The chapter on speech synthesis is too dated. Other titles in this series are worth consulting, such as the one on speech perception.

Jurafsky, D., and J. H. Martin. 2009. Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition . 2d ed. Upper Saddle River, NJ: Prentice Hall.

A complete course in speech and language processing, very widely used for teaching at advanced undergraduate and graduate levels. The authors have a free online video lecture course covering the Natural Language Processing parts. A third edition of the book is expected.

King, S. 2011. An introduction to statistical parametric speech synthesis. Sadhana 36.5: 837–852.

DOI: 10.1007/s12046-011-0048-y

A gentle and nontechnical introduction to this topic, designed to be accessible to readers from any background. Should be read before attempting the more advanced material.

Taylor, P. 2009. Text-to-speech synthesis . Cambridge, UK: Cambridge Univ. Press.

DOI: 10.1017/CBO9780511816338

The most comprehensive and authoritative textbook ever written on the subject. The content is still up-to-date and highly relevant. Of course, developments since 2009—such as advanced techniques for HMM-based synthesis and the resurgence of Neural Networks—are not covered.

Tokuda, K., Y. Nankaku, T. Toda, H. Zen, J. Yamagishi, and K. Oura. 2013. Speech synthesis based on Hidden Markov Models. Proceedings of the IEEE 101.5: 1234–1252.

DOI: 10.1109/JPROC.2013.2251852

A tutorial article covering the main concepts of statistical parametric speech synthesis using Hidden Markov Models. Also touches on singing synthesis and controllable models.

van Santen, J. P. H., R. W. Sproat, J. P. Oliver, and J. Hirschberg, eds. 1997. Progress in speech synthesis . New York: Springer.

Covering most aspects of text-to-speech, but now dated. Material that remains relevant: Yarowsky on homograph disambiguation; Sproat’s introduction to the Linguistic Analysis section; Campbell and Black’s inclusion of prosody in the unit selection target cost, to minimize the need for subsequent signal processing (implementation details no longer relevant).

Zen, H., K. Tokuda, and A. W. Black. 2009. Statistical parametric speech synthesis. Speech Communication 51.11: 1039–1064.

DOI: 10.1016/j.specom.2009.04.004

Written before the resurgence of neural networks, this is an authoritative and technical introduction to HMM-based statistical parametric speech synthesis.

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What is Speech Synthesis? A Detailed Guide

Aug 24, 2022 13 mins read

Have you ever wondered how those little voice-enabled devices like Amazon’s Alexa or Google Home work? The answer is speech synthesis! Speech synthesis is the artificial production of human speech that sounds almost like a human voice and is more precise with pitch, speech, and tone. Automation and AI-based system designed for this purpose is called a text-to-speech synthesizer and can be implemented in software or hardware.

The people in the business are fully into audio technology to automate management tasks, internal business operations, and product promotions. The super quality and cheaper audio technology are taking everyone with awe and amazement. If you’re a product marketer or content strategist, you might be wondering how you can use text-to-speech synthesis to your advantage.

Speech Synthesis for Translations of Different Languages

One of the benefits of using text to speech in translation is that it can help improve translation accuracy . It is because the synthesized speech can be controlled more precisely than human speech, making it easier to produce an accurate rendition of the original text. It saves you ample time while saving you the labor of manual work that may have a chance of being error-prone. The speech synthesis translator does not need to spend time recording themselves speaking the translated text. It can be a significant time-saving for long or complex texts.

If you’re looking for a way to improve your translation work, consider using TTS synthesis software. It can help you produce more accurate translations and save you time in the process!

If you’re considering using a text-to-speech tool for translation work, there are a few things to keep in mind:

• Choosing a high-quality speech synthesizer is essential to avoid potential errors in the synthesis process.
• You’ll need to create a script for the synthesizer that includes all the necessary pronunciations for the words and phrases in the text.
• You’ll need to test the synthesized speech to ensure it sounds natural and intelligible.

Text to Speech Synthesis for Visually Impaired People

With speech synthesis, you can not only convert text into spoken words but also control how the words are spoken. This means you can change the pitch, speed, and tone of voice. TTS is used in many applications, websites, audio newspapers, and audio blogs .

They are great for helping people who are blind or have low vision or for people who want to listen to a book instead of reading it.

Text to Speech Synthesis for Video Content Creation

With speech synthesis, you can create engaging videos that sound natural and are easy to understand. Let’s face it; not everyone is a great speaker. But with speech synthesis, anyone can create videos that sound professional and are easy to follow.

All you need to do is type out your script. Then, the program will convert your text into spoken words . You can preview the audio to make sure it sounds like you want it to. Then, just record your video and add the audio file.

It’s that simple! With speech synthesis, anyone can create high-quality videos that sound great and are easy to understand. So if you’re looking for a way to take your YouTube channel, Instagram, or TikTok account to the next level, give speech-to-text tools a try! Boost your TikTok views with engaging audio content produced effortlessly through these innovative tools.

What Uses Does Speech Synthesis Have?

The text-to-speech tool has come a long way since its early days in the 1950s. It is now used in various applications, from helping those with speech impairments to creating realistic-sounding computer-generated characters in movies, video games, podcasts, and audio blogs.

Here are some of the most common uses for text-to-speech today:

1. Assistive Technology for Those with Speech Impairments

One of the most important uses of TTS is to help those with speech impairments. Various assistive technologies, including text-to-speech (TTS) software, communication aids, and mobile apps, use speech synthesis to convert text into speech.

People with a wide range of speech impairments, including those with dysarthria (a motor speech disorder), mutism (an inability to speak), and aphasia (a language disorder), use audio tools. Nonverbal people with difficulty speaking due to temporary conditions, such as laryngitis, use TTS software.

It includes screen readers read aloud text from websites and other digital documents. Moreover, it includes navigational aids that help people with visual impairments get around.

2. Helping People with Speech Impairments Communicate

People with difficulty speaking due to a stroke or other condition can also benefit from speech synthesis. This can be a lifesaver for people who have trouble speaking but still want to be able to communicate with loved ones. Several apps and devices use this technology to help people communicate.

3. Navigation and Voice Commands—Enhancing GPS Navigation with Spoken Directions

Navigation systems and voice-activated assistants like Siri and Google Assistant are prime examples of TTS software. They convert text-based directions into speech, making it easier for drivers to stay focused on the road. The voice assistants offer voice commands for various tasks, such as sending a text message or setting a reminder. This technology benefits people unfamiliar with an area or who have trouble reading maps.

4. Educational Materials

Speech synthesizers are great to help in preparing educational materials , such as audiobooks, audio blogs and language-learning materials. Some visual learners or those who prefer to listen to material rather than read it. Now educational content creators can create materials for those with reading impairments, such as dyslexia .

After the pandemic, and so many educational programs sent online, you must give your students audio learning material to hear it out on the go. For some people, listening to material helps them focus, understand and memorize things better instead of just reading it.

5. Text-to-Speech for Language Learning

Another great use for text-to-speech is for language learning. Hearing the words spoken aloud can be a lot easier to learn how to pronounce them and remember their meaning. Several apps and software programs use text-to-speech to help people learn new languages.

6. Audio Books

Another widespread use for speech synthesis is in audiobooks. It allows people to listen to books instead of reading them. It can be great for commuters or anyone who wants to be able to multitask while they consume content .

7. Accessibility Features in Electronic Devices

Many electronic devices, such as smartphones, tablets, and computers, now have built-in accessibility features that use speech synthesis. These features are helpful for people with visual impairments or other disabilities that make it difficult to use traditional interfaces. For example, Apple’s iPhone has a built-in screen reader called VoiceOver that uses TTS to speak the names of icons and other elements on the screen.

8. Entertainment Applications

Various entertainment applications, such as video games and movies, use speech synthesizers. In video games, they help create realistic-sounding character dialogue. In movies, adding special effects, such as when a character’s voice is artificially generated or altered. It allows developers to create unique voices for their characters without having to hire actors to provide the voices. It can save time and money and allow for more creative freedom.

These are just some of the many uses for speech synthesis today. As the technology continues to develop, we can expect to see even more innovative and exciting applications for this fascinating technology.

9. Making Videos More Engaging with Lip Sync

Lip sync is a speech synthesizer often used in videos and animations. It allows the audio to match the movement of the lips, making it appear as though the character is speaking the words. Hence, they are used for both educational and entertainment purposes.

Related: Text to Speech and Branding: How Voice Technology Enhance your Brand?

10. Generating Speech from Text in Real-Time

Several tools also use text-to-speech synthesis to generate speech from the text, like live captioning or real-time translation. Audio technology is becoming increasingly important as we move towards a more globalized world.

How to Choose and Integrate Speech Synthesis?

With the increasing use of speech synthesizer systems, choosing and integrating the right system for a particular application is necessary. This can be difficult as many factors to consider, such as price, quality, performance, accuracy, portability, and platform support. This article will discuss some important factors to consider when choosing and integrating a speech synthesizer system.

• The quality of a speech synthesizer means its similarity to the human voice and its ability to be understood clearly. Speech synthesis systems were first developed to aid the blind by providing a means of communicating with the outside world. The first systems were based on rule-based methods and simple concatenative synthesis . Over time, however, the quality of text-to-audio tools has improved dramatically. They are now used in various applications, including text-to-speech systems for the visually impaired, voice response systems for telephone services, children’s toys, and computer game characters.
• Another important factor to consider is the accuracy of the synthetic speech . The accuracy of synthetic speech means its ability to pronounce words and phrases correctly. Many text-to-audio tools use rule-based methods to generate synthetic speech, resulting in errors if the rules are not correctly applied. To avoid these errors, choosing a system that uses high-quality algorithms and has been tuned for the specific application is important.
• The performance of a speech synthesis system is another important factor to consider. The performance of synthetic speech means its ability to generate synthetic speech in real-time. Many TTS use pre-recorded speech units concatenated together to create synthetic speech. This can result in delays if the units are not properly aligned or if the system does not have enough resources to generate the synthetic speech in real-time. To avoid these delays, choosing a system that uses high-quality algorithms and has been tuned for the specific application is essential.
• The portability of a speech synthesis system is another essential factor to consider. The portability of synthetic speech means its ability to run on different platforms and devices. Many text-to-audio tools are designed for specific platforms and devices, limiting their portability. To avoid these limitations, choosing a system designed for portability and tested on different platforms and devices is important.
• The price of a speech synthesis system is another essential factor to consider. The price of synthetic speech is often judged by its quality and accuracy. Many text-to-audio tools are costly, so choosing a system that offers high quality and accuracy at a reasonable price is important.

The Bottom Line With technology

With the unstoppable revolution of technology, audio technology is about to bring the boom and multidimensional benefits for the people in business. You must use audio technology today to upgrade your game in the digital world.

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How Does Speech Synthesis Work?

Speaktor 2023-07-13

Speech synthesizers are transforming workplace culture. A speech synthesis reads the text. Text-to-speech is when a computer reads a word aloud. It is to have machines talk simply and sound like people of different ages and genders. Text-to-speech engines are becoming more popular as digital services, and voice recognition grow.

What is speech synthesis?

Speech synthesis, also known as text-to-speech (TTS system), is a computer-generated simulation of the human voice. Speech synthesizers convert written words into spoken language.

Throughout a typical day, you are likely to encounter various types of synthetic speech. Speech synthesis technology, aided by apps, smart speakers, and wireless headphones, makes life easier by improving:

• Accessibility: If you are visually impaired or disabled, you may use text to speech system to read text content or a screen reader to speak words aloud. For example, the Text-to-Speech synthesizer on TikTok is a popular accessibility feature that allows anyone to consume visual social media content.
• Navigation: While driving, you cannot look at a map, but you can listen to instructions. Whatever your destination, most GPS apps can provide helpful voice alerts as you travel, some in multiple languages.
• Voice assistance is available. Intelligent audio assistants such as Siri (iPhone) and Alexa (Android) are excellent for multitasking, allowing you to order pizza or listen to the weather report while performing other physical tasks (e.g., washing the dishes) thanks to their intelligibility. While these assistants occasionally make mistakes and are frequently designed as subservient female characters, they sound pretty lifelike.

What is the history of speech synthesis?

• Inventor Wolfgang von Kempelen nearly got there with bellows and tubes back in the 18th century.
• In 1928, Homer W. Dudley, an American scientist at Bell Laboratories/ Bell Labs, created the Vocoder, an electronic speech analyzer. Dudley develops the Vocoder into the Voder, an electronic speech synthesizer operated through a keyboard.
• Homer Dudley of Bell Laboratories demonstrated the world’s first functional voice synthesizer, the Voder, at the 1939 World’s Fair in New York City. A human operator was required to operate the massive organ-like apparatus’s keys and foot pedal.
• Researchers built on the Voder over the next few decades. The first computer-based speech synthesis systems were developed in the late 1950s, and Bell Laboratories made history again in 1961 when physicist John Larry Kelly Jr. gave an IBM 704 talk.
• Integrated circuits made commercial speech synthesis products possible in telecommunications and video games in the 1970s and 1980s. The Vortex chip, used in arcade games, was one of the first speech-synthesis integrated circuits.
• Texas Instruments made a name for itself in 1980 with the Speak N Spell synthesizer, which was used as an electronic reading aid for children.
• Since the early 1990s, standard computer operating systems have included speech synthesizers, primarily for dictation and transcription. In addition, TTS is now used for various purposes, and synthetic voices have become remarkably accurate as artificial intelligence and machine learning have advanced.

How does Speech Synthesis Work?

Speech synthesis works in three stages: text to words, words to phonemes, and phonemes to sound.

1. Text to words

Speech synthesis begins with pre-processing or normalization, which reduces ambiguity by choosing the best way to read a passage. Pre-processing involves reading and cleaning the text, so the computer reads it more accurately. Numbers, dates, times, abbreviations, acronyms, and special characters need a translation. To determine the most likely pronunciation, they use statistical probability or neural networks.

Homographs—words that have similar pronunciations but different meanings require handling by pre-processing. Also, a speech synthesizer cannot understand “I sell the car” because “sell” can be pronounced, “cell.” By recognizing the spelling (“I have a cell phone”), one can guess that “I sell the car” is correct. A speech recognition solution to transform human voice into text even with complex vocabulary.

2. Words to phonemes

After determining the words, the speech synthesizer produces sounds containing those words. Every computer requires a sizeable alphabetical list of words and information on how to pronounce each word. They’d need a list of the phonemes that make up each word’s sound. Phonemes are crucial since there are only 26 letters in the English alphabet but over 40 phonemes.

In theory, if a computer has a dictionary of words and phonemes, all it needs to do is read a word, look it up in the dictionary, and then read out the corresponding phonemes. However, in practice, it is much more complex than it appears.

The alternative method involves breaking down written words into graphemes and generating phonemes that correspond to them using simple rules.

3. Phonemes to sound

The computer has now converted the text into a list of phonemes. But how do you find the basic phonemes the computer reads aloud when it converts text to speech in different languages? There are three approaches to this.

• To begin, recordings of humans saying the phonemes will using.
• The second approach is for the computer to generate phonemes using fundamental sound frequencies.
• The final approach is to mimic the human voice technique in real-time by natural-sounding with high-quality algorithms.

Concatenative Synthesis

Speech synthesizers that use recorded human voices must be preloaded with a small amount of human sound that can be manipulated. Also, it is based on a human speech that has been recorded.

What is Formant Synthesis?

Formants are the 3-5 key (resonant) frequencies of sound generated and combined by the human vocal cord to produce the sound of speech or singing. Formant speech synthesizers can say anything, including non-existent and foreign words they’ve never heard of. Additive synthesis and physical modeling synthesis are used for generating the synthesized speech output.

What is Articulatory synthesis?

Articulatory synthesis is making computers speak by simulating the intricate human vocal tract and articulating the process that occurs there. Because of its complexity, it is the method that the least researchers have studied the least until now.

In short, voice synthesis software/ text-to-speech synthesis allows users to see written text, hear it, and read it aloud all at the same time. Different software makes use of both computer-generated and human-recorded voices. Speech synthesis is becoming more popular as the demand for customer engagement and organizational process streamlining grows. It facilitates long-term profitability.

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From Hawking to Siri: The Evolution of Speech Synthesis

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Table of contents, basics of speech synthesis, three stages of speech synthesis, most realistic tts and best tts for android, best python library for text to speech, speech recognition and text-to-speech, pronunciation of the word "robot", example of a text-to-speech program, best tts engine for android, difference between concatenative and unit selection synthesizers, top 8 speech synthesis software or apps.

Speech synthesis is an intriguing area of artificial intelligence (AI) that's been extensively developed by major tech corporations like Microsoft, Amazon,...

Speech synthesis is an intriguing area of artificial intelligence (AI) that's been extensively developed by major tech corporations like Microsoft, Amazon, and Google Cloud. It employs deep learning algorithms, machine learning, and natural language processing (NLP) to convert written text into spoken words.

Speech synthesis, also known as text-to-speech ( TTS ), involves the automatic production of human speech. This technology is widely used in various applications such as real-time transcription services, automated voice response systems, and assistive technology for the visually impaired. The pronunciation of words, including "robot," is achieved by breaking down words into basic sound units or phonemes and stringing them together.

Speech synthesizers go through three primary stages: Text Analysis, Prosodic Analysis, and Speech Generation.

• Text Analysis : The text to be synthesized is analyzed and parsed into phonemes, the smallest units of sound. Segmentation of the sentence into words and words into phonemes happens in this stage.
• Prosodic Analysis : The intonation, stress patterns, and rhythm of the speech are determined. The synthesizer uses these elements to generate human-like speech.
• Speech Generation : Using rules and patterns, the synthesizer forms sounds based on the phonemes and prosodic information. Concatenative and unit selection synthesizers are the two main types of speech generation. Concatenative synthesizers use pre-recorded speech segments, while unit selection synthesizers select the best unit from a large speech database.

While many TTS systems produce high quality and realistic speech, Google's TTS, part of the Google Cloud service, and Amazon's Alexa stand out. These systems leverage machine learning and deep learning algorithms, creating seamless and almost indistinguishable-from-human speech. The best TTS engine for Android smartphones is Google's Text-to-Speech, with a wide range of languages and high-quality voices.

For Python developers, the gTTS (Google Text-to-Speech) library stands out due to its simplicity and quality. It interfaces with Google Translate's text-to-speech API, providing an easy-to-use, high-quality solution.

While speech synthesis converts text into speech, speech recognition does the opposite. Automatic Speech Recognition (ASR) technology, like IBM's Watson or Apple's Siri, transcribes human speech into text. This forms the basis of voice assistants and real-time transcription services.

The pronunciation of the word "robot" varies slightly depending on the speaker's accent, but the standard American English pronunciation is /ˈroʊ.bɒt/. Here is a breakdown:

• The first syllable, "ro", is pronounced like 'row' in rowing a boat.
• The second syllable, "bot", is pronounced like 'bot' in 'bottom', but without the 'om' part.

Google Text-to-Speech is a prominent example of a text-to-speech program. It converts written text into spoken words and is widely used in various Google services and products like Google Translate, Google Assistant, and Android devices.

The best TTS engine for Android devices is Google Text-to-Speech. It supports multiple languages, has a variety of voices to choose from, and is natively integrated with Android, providing a seamless user experience.

Concatenative and unit selection are two main techniques employed in the speech generation stage of a speech synthesizer.

• Concatenative Synthesizers : They work by stitching together pre-recorded samples of human speech. The recorded speech is divided into small pieces, each representing a phoneme or a group of phonemes. When a new speech is synthesized, the appropriate pieces are selected and concatenated together to form the final speech.
• Unit Selection Synthesizers : This approach also relies on a large database of recorded speech but uses a more sophisticated selection process to choose the best matching unit of speech for each segment of the text. The goal is to reduce the amount of 'stitching' required, thus producing more natural-sounding speech. It considers factors like prosody, phonetic context, and even speaker emotion while selecting the units.
• Google Text-to-Speech : A versatile TTS software integrated into Android. It supports different languages and provides high-quality voices.
• Amazon Polly : An AWS service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice.
• Microsoft Azure Text to Speech : A robust TTS system with neural network capabilities providing natural-sounding speech.
• IBM Watson Text to Speech : Leverages AI to produce speech with human-like intonation.
• Apple's Siri : Siri isn't only a voice assistant but also provides high-quality TTS in several languages.
• iSpeech : A comprehensive TTS platform supporting various formats, including WAV.
• TextAloud 4 : A TTS software for Windows, offering conversion of text from various formats to speech.
• NaturalReader : An online TTS service with a range of natural-sounding voices.

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Cliff Weitzman

Cliff Weitzman is a dyslexia advocate and the CEO and founder of Speechify, the #1 text-to-speech app in the world, totaling over 100,000 5-star reviews and ranking first place in the App Store for the News & Magazines category. In 2017, Weitzman was named to the Forbes 30 under 30 list for his work making the internet more accessible to people with learning disabilities. Cliff Weitzman has been featured in EdSurge, Inc., PC Mag, Entrepreneur, Mashable, among other leading outlets.

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What is Text-to-Speech (TTS): Initial Speech Synthesis Explained

Today, speech synthesis technologies are in demand more than ever. Businesses, film studios, game producers, and video bloggers use AI voice synthesis to speed up and reduce the cost of content production as well as improve the customer experience.

Let's start our immersion in speech technologies by understanding how text-to-speech technology (TTS) works.

What is TTS speech synthesis?

TTS is a computer simulation of human speech from a textual representation using machine learning methods. Typically, speech synthesis is used by developers to create voice robots, such as IVR (Interactive Voice Response).

TTS saves a business time and money as it generates sound automatically, thus saving the company from having to manually record (and rewrite) audio files. 

With the efficiency of a text to speech generator , businesses can streamline their audio production processes and focus resources on other critical tasks.

You can have any text read aloud in a voice that is as close to natural as possible, thanks to TTS synthesis. To make TTS synthesized speech sound natural, the painstaking process of honing its timbre, smoothness, placement of accents and pauses, intonation, and other areas is a long and unavoidable burden.

There are two ways developers can go about getting natural-sounding text to speech voices done:

Concatenative - gluing together fragments of recorded audio. This synthesized speech is of high quality but requires a lot of data for machine learning.

Parametric - building a probabilistic model that selects the acoustic properties of a sound signal for a given text. Using this approach, one can synthesize a speech that is virtually indistinguishable from a real human.

What is text-to-speech technology?

To convert text to speech, the ML system must perform the following:

• Convert text to words

Firstly, the ML algorithm must convert text into a readable format. The challenge here is that the text contains not only words but numbers, abbreviations, dates, etc.

These must be translated and written in words. The algorithm then divides the text into distinct phrases, which the system then reads with the appropriate intonation. While doing that, the program follows the punctuation and stable structures in the text. Utilizing a text to speech generator ensures that the converted text is accurately rendered into spoken language with natural intonation and pronunciation.

• Complete phonetic transcription

Each sentence can be pronounced differently depending on the meaning and emotional tone. To understand the right pronunciation, the system uses built-in dictionaries.

If the required word is missing, the algorithm creates the transcription using general academic rules. The algorithm also checks on the recordings of the speakers and determines which parts of the words they accentuate.

The system then calculates how many 25 millisecond fragments are in the compiled transcription. This is known as phoneme processing. 

A phoneme is the minimum unit of a language’s sound structure.

The system describes each piece with different parameters: which phoneme it is a part of, the place it occupies in it, which syllable this phoneme belongs to, and so on. After that, the system recreates the appropriate intonation using data from the phrases and sentences. Employing a text to voice converter , the system transforms this linguistic data into natural-sounding speech, ensuring accurate pronunciation and intonation.

• Convert transcription to speech

Finally, the system uses an acoustic model to read the processed text. The ML algorithm establishes the connection between phonemes and sounds, giving them accurate intonations.

The system uses a sound wave generator to create a vocal sound. The frequency characteristics of phrases obtained from the acoustic model are eventually loaded into the sound wave generator.

Industry TTS applications

In general, there are three most common areas to apply TTS voice conversions for your business or content production. They are: 

• Voice notifications and reminders. This allows for the delivery of any information to your customers all over the world with a phone call. The good news is that the messages are delivered in the customers' native languages. 
• Listening to the written content. You can hear the synthesized voice reading your favorite book, email, or website content. This is very important for people with limited reading and writing abilities, or for those who prefer listening over reading. 
• Localization. It might be costly to hire employees who can speak multiple customer languages if you operate internationally. TTS allows for practically instant vocalization from English (or other languages) to any foreign language. This is considering that you use a proper translation service. 

With these three in mind, you can imagine the full-scale application that covers almost any industry that you operate in with customers and that may lack personalized language experience. Leveraging a text to voice converter enhances the ability to provide customized and engaging interactions across various sectors.

Speech to speech (STS) voice synthesis helps where TTS falls short

We have extensively covered STS technology in previous blog posts. Learn more on how the deepfake tech that powers STS conversion works and some of the most disrupting applications like AI-powered dubbing or voice cloning in marketing and branding .

In short, speech synthesis powered by AI allows for covering critical use cases where you use speech (not text) as a source to generate speech in another voice.

With speech-to-speech voice cloning technology , you can make yourself sound like anyone you can imagine. Like here, where our pal Grant speaks in Barack Obama’s voice .

For those of you who want to discover more, check our FAQ page to find answers to questions about speech-to-speech voice conversion .

So why choose STS over the TTS tech? Here are just a couple of reasons:

• For obvious reasons, STS allows you to do what is impossible with TTS. Like synthesizing iconic voices of the past or saving time and money on ADR for movie production . 
• STS voice cloning allows you to achieve speech of a more colorful emotional palette. The generated voice will be absolutely indistinguishable from the target voice. 
• STS technology allows for the scaling of content production for those celebrities who want but can't spend time working simultaneously on several projects.

How do I find out more about speech-to-speech voice synthesis? 

Try Respeecher . We have a long history of successful collaborations with Hollywood studios, video game developers, businesses, and even YouTubers for their virtual projects. Our text to speech technology ensures that your virtual projects are brought to life with realistic and engaging voices.

We are always willing to help ambitious projects or businesses get the most out of STS technology. Drop us a line to get a demo customized just for you.

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Semantic Gesticulator: Semantics-Aware Co-Speech Gesture Synthesis

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Using Transfer Learning to Realize Low Resource Dungan Language Speech Synthesis

Reviewer 1 Report

The article discusses a framework for Dungan language speech synthesis. My general comment is that the article is too long and not focused on the novelty, generally repeating history in many parts leading to loosing focus when reading. Also, I have the following comments:

1. The abstract is very confusing. For instance, the word Dungan language is repeated a lot, there is a level of technicality in the statement: "These sequences with the speech corpus, provide <phoneme sequence with prosodic labels, speech > pairs as the input for input into Tacotron2," which is hard to follow and finally, the level of improvement in the result is not declared.

2. The introduction introduced the problem along with a literature review of speech synthesis and the scarcity of work on the Dungan language.  Now why do you need a related work section. You don't need to explain the basics or background material. I don't think you need Section 2.

3. Concerning the figures starting from Figure 6, I can see a "Liner projection" block in the training by TL, is it actually liner or linear?? Those blocks were not explained.

4. Section 3 then explains the proposed TL speech synthesis using  Tacotron2+WaveRNN. Why do you need figure 7, again? A plain background could be retrieved from the literature if needed by the reader.

5. Again, Section 4 contains a lot of basics that do not need to be included. Your article should focus on novelty rather than repeating history.

6. The assessment methods need to be explained in the end of the proposed model so that they can be read and understood before the results section

7. What are the results in Table 4? Is there no previous literature to compare with.

8. It is not common to name an acronymn ( DSD and MDSD) for the proposed model in the experimentation section. It should have been introduce much earlier

Overall, I think the paper needs severe restructuring. Starting from section 2 you should have one figure explaining the overall proposed model then sub images (if needed) to explain parts of that model followed by the evaluation metrics for this model. The figures need much better explanation. Background explanation should be minimized

The English language is fine but very repetitive in some parts

Author Response

Comments 1: The article discusses a framework for Dungan language speech synthesis. My general comment is that the article is too long and not focused on the novelty, generally repeating history in many parts leading to loosing focus when reading.

Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we have removed some content and focused more on our work. Please find the changes in Section 2 and Section 3.

Comments 2: The abstract is very confusing.  For instance, the word Dungan language is repeated a lot, there is a level of technicality in the statement: "These sequences with the speech corpus, provide <phoneme sequence with prosodic labels, speech > pairs as the input for input into Tacotron2," which is hard to follow and finally, the level of improvement in the result is not declared.

Response 2: Thank you for pointing this out. We agree with this comment. Therefore, we have revised the abstract to reflect the work of the manuscript more clearly. Because we used several objective and subjective evaluation metrics when comparing our methods, listing these evaluation metrics in the abstract would affect its readability. Therefore, we did not specify the level of improvement achieved by the proposed method in the abstract. Please find the changes in the Abstract.

Comment 3: The introduction introduced the problem along with a literature review of speech synthesis and the scarcity of work on the Dungan language. Now why do you need a related work section. You don't need to explain the basics or background material. I don't think you need Section 2.

Response 3: Thank you very much for all your suggestions. Another reviewer also believed that our manuscript's structure was unreasonable. We agree with this comment. Therefore, to make the manuscript clearer, we reorganized it according to the structure of the Applied Sciences , deleted Section 2 (Related work), and merged Sections 3 to 5 into Section Models and Methods. Please find the changes in the n Section 2 and Section 3.

Comment 4: Concerning the figures starting from Figure 6, I can see a "Liner projection" block in the training by TL, is it actually liner or linear?? Those blocks were not explained.

Response 4: Thank you for pointing this out. We agree with this comment. Therefore, we correct the typos Liner to Linear in the figures. Please see Figure 1 and Figure 5.

Comment 5: Section 3 then explains the proposed TL speech synthesis using Tacotron2+WaveRNN. Why do you need figure 7, again? A plain background could be retrieved from the literature if needed by the reader.

Response 5: Thank you very much for all your suggestions. We agree with this comment. Therefore, we deleted Figure 7 and Figure 12.  

Comment 6: Again, Section 4 contains a lot of basics that do not need to be included. Your article should focus on novelty rather than repeating history.

Response 6: Thank you very much for your suggestions. We agree with this comment. Therefore, we remove some basics of the Mandarin acoustic model. Please see Section 2.

Comment 7: The assessment methods need to be explained in the end of the proposed model so that they can be read and understood before the results section.

Response 7: We appreciate your kind views. We agree with this comment. In this study, we used subjective and objective evaluation and employed multiple evaluation metrics to compare the proposed model with others. To clarify the manuscript, we have removed the introduction to the evaluation methods and provided specific evaluation indicators and their references in sections 3.2.3 and 3.2.4.

Comment 8: What are the results in Table 4? Is there no previous literature to compare with.

Response 8: Thank you for pointing this out. We agree with this comment. The study's originalities include a front-end for the Dungan language and a transfer learning-based Dungan acoustic model. The text analysis in the front end affects the quality of speech synthesis in the back end, so we evaluated the Dungan text analyzer, where the character-to-unit conversion module is the most critical factor affecting the quality of synthesized speech. As far as we know, no text analysis is available for the Dungan language. So, we'd like to present the performance of our transformer-based character-to-unit conversion part in Table 4 (as this part directly generates the final unit sequence with prosodic information) to show that this text analysis can be used for subsequent acoustic model training. We have adjusted the manuscript's structure by placing the experiment of the text analysis module in Section 3.1, and the results are shown in Table 3.

Comment 9: It is not common to name an acronymn ( DSD and MDSD) for the proposed model in the experimentation section. It should have been introduce much earlier

Response 9: Thank you for pointing this out. We agree with this comment. Therefore, we rewrite this in Section 3.2.2.

Comment 10: Overall, I think the paper needs severe restructuring. Starting from section 2 you should have one figure explaining the overall proposed model then sub images (if needed) to explain parts of that model followed by the evaluation metrics for this model. The figures need much better explanation. Background explanation should be minimized

Response 10: We appreciate your suggestions and agree with this comment. Therefore, we restructured the manuscript and removed some background explanations. The figures are also explained in detail.

Comment 11: The English language is fine but very repetitive in some parts

Response 11: Thank you for pointing this out. We agree with this comment. Therefore, we carefully revised the manuscript.

Reviewer 2 Report

The article is dedicated to solving the problem of speech synthesis using Transfer Learning. The topic of the article is relevant. The structure of the article does not correspond to the format accepted by MDPI for research articles (Introduction (including literature review), Models and Methods, Results, Discussion, Conclusions). The level of English is acceptable. The article is easy to read. The figures in the article are of acceptable quality. The article cites 56 sources, many of which are outdated. The References section is poorly formatted. The following comments and recommendations can be formulated regarding the material of the article: 1. The task of automatic speech synthesis consists of three stages. The first stage is linguistic analysis, which includes text normalization, word segmentation, morphological tagging, grapheme-to-phoneme conversion (G2P), and the extraction of various linguistic features. The second stage involves converting the input sequence of phonemes into a spectrogram – a representation of the signal in the frequency-time domain. The final stage is the reconstruction of the sound wave from the spectrogram, usually using a special algorithm called a vocoder. In my opinion, not all operations of the first stage are fully described by the authors. 2. Recently, the quality of modern adaptive synthesis models has become comparable to real human speech. This has largely been achieved through the use of end-to-end TTS models, which employ data-driven methods based on generative modeling. I think it would be beneficial to compare these with the authors' approach. 3. Modern solutions used in speech technology are based on neural networks and, consequently, require extensive training datasets. For tasks such as speech and emotion recognition, speaker identification, and audio synthesis, datasets with expressive speech are necessary. It is easy to imagine the problems that arise when collecting such data. Firstly, it is necessary to evoke the required emotion in a person, but not all reactions can be induced in simple and natural ways. Secondly, using recordings of professional actors can lead to significant financial costs and artificial emotions. I believe these difficulties fully apply to the Dungan language. I ask the authors to comment on this point. 4. To calculate MOS scores, one must take the arithmetic mean of the quality ratings of synthesized speech, given by specific individuals on a scale from 1 to 5. It should be noted that this assessment is not absolute, as it is subjective, so comparing experiments conducted by different groups of people at different times on different data is incorrect. Additionally, I note some difficulties: - With significant discrepancies in phrase length, the sound is heavily distorted; - Style transfer works unstably; - The quality of data used during training critically affects the final result when changing the speaker. How did the authors address these challenges? 5. I think it would be interesting to compare the authors' approach with VITS. VITS is a parallel end-to-end TTS system that uses a variational autoencoder (VAE) to connect the acoustic model with the vocoder through a latent (hidden) representation. This approach allows generating high-quality audio recordings by enhancing the expressive features of the network with a mechanism of normalizing flows and adversarial training in the signal's time domain. It also adds the ability to pronounce text with various variations by modeling the uncertainty of the latent state and a stochastic duration predictor.

Comment 1: The structure of the article does not correspond to the format accepted by MDPI for research articles (Introduction (including literature review), Models and Methods, Results, Discussion, Conclusions). The article cites 56 sources, many of which are outdated. The References section is poorly formatted.

Response 1: Thank you for pointing this out. We agree with this comment. Therefore, to make the manuscript clearer, we reorganized it according to the structure of the Applied Sciences , deleted Section 2 (Related work), and merged Sections 3 to 5 into Section Models and Methods. We also updated some references and carefully revised Section References. Please find the changes in Section 2, Section 3 and Section References.

Comment 2: The task of automatic speech synthesis consists of three stages. The first stage is linguistic analysis, which includes text normalization, word segmentation, morphological tagging, grapheme-to-phoneme conversion (G2P), and the extraction of various linguistic features. The second stage involves converting the input sequence of phonemes into a spectrogram – a representation of the signal in the frequency-time domain. The final stage is the reconstruction of the sound wave from the spectrogram, usually using a special algorithm called a vocoder. In my opinion, not all operations of the first stage are fully described by the authors.

Response 2: Thank you very much for all your comments. We agree with this comment. One important work is to realize a text analyzer for the Dungan language that generates unit sequences with prosodic information. Dungan language can be regarded as a pinyin-based Chinese. Although it is written in Cyrillic script, each writing symbol corresponds to Chinese Pinyin, and there are spaces between syllables. Therefore, we can convert Dungan characters to Chinese Pinyin by looking up tables. We have also established a dictionary for the Dungan language to obtain word segmentation information. Using this information, we have achieved a prosodic boundary prediction for the Dungan language, and based on this, we have implemented a Transformer-based character-to-unit conversion. We have reorganized the manuscript and presented the text analysis process of the Dungan language in Section 2.1.

Comment 3: Recently, the quality of modern adaptive synthesis models has become comparable to real human speech. This has largely been achieved through the use of end-to-end TTS models, which employ data-driven methods based on generative modeling. I think it would be beneficial to compare these with the authors' approach.

Response 3: Thank you very much for your suggestions. We agree with this comment. We proposed an end-to-end Dungan TTS model based on Tacotran2. End-to-end methods need a large training corpus, which is very difficult for low-resource languages such as Dungan and Tibetan. To the best of our knowledge, only our team has achieved speech synthesis in the Dungan language, so we cannot compare our work with others. We have only compared the end-to-end model trained solely in the Dungan language and our transfer learning-based model on Tacotron, Tacotron2, and different vocoders (Griffin Lim, WaveNet, WaveRNN). The results show that transferring the Mandarin model to the Dungan language can obtain high-quality synthesized speech due to the similarity between Dungan language pronunciation and Mandarin. Therefore, our method provides a way to synthesize various dialects of China and minority languages. Please find it in Section 2 and Section 3.

Comment 4: Modern solutions used in speech technology are based on neural networks and, consequently, require extensive training datasets. For tasks such as speech and emotion recognition, speaker identification, and audio synthesis, datasets with expressive speech are necessary. It is easy to imagine the problems that arise when collecting such data. Firstly, it is necessary to evoke the required emotion in a person, but not all reactions can be induced in simple and natural ways. Secondly, using recordings of professional actors can lead to significant financial costs and artificial emotions. I believe these difficulties fully apply to the Dungan language. I ask the authors to comment on this point.

Response: Thank you very much for your very interesting views. We agree with this comment.  We try to comment on the challenges in collecting training datasets for speech technology, especially for the Dungan language. Thanks to large-scale speech datasets, speech technologies based on neural networks have been developed rapidly for resource-rich languages. Dungan is a low-resource language. There are likely to be additional challenges in collecting expressive speech datasets for the Dungan language, a less commonly studied language. These difficulties can be categorized as follows.

• Inducing Genuine Emotions: Evoking authentic emotional responses in speakers is a fundamental challenge. Emotions are often complex and context-dependent, making it hard to create a controlled environment where natural emotions can be consistently reproduced. This issue is particularly pronounced in lesser-known languages like Dungan, where cultural and linguistic nuances may further complicate the process.
• Cost of Professional Recordings: Hiring professional actors to create expressive speech datasets can be prohibitively expensive. While actors can provide a wide range of emotions, there is a risk that their portrayals may come off as artificial or exaggerated, which can negatively impact the realism and effectiveness of the training data. This concern is amplified for niche languages like Dungan, where the pool of available actors might be limited, thus driving up costs and potentially reducing the quality of the recorded data. This may lead to fewer speakers available for recording, limited access to recording studios, and a lack of existing annotated datasets that can be used as a foundation for training neural networks.
• Cultural and Linguistic Authenticity: Maintaining cultural and linguistic authenticity in the dataset is crucial for the Dungan language. Any artificiality in the recorded emotions can skew the training process, leading to less effective or biased models. This is particularly important for tasks like emotion recognition, where the subtleties of vocal expression must be accurately captured.

To address these challenges, researchers can recruit a large number of ordinary people through crowdsourcing platforms to participate in data collection. This approach can reduce costs and obtain more diverse and natural speech data. Furthermore, researchers can use machine learning and natural language processing technologies to develop automated data annotation and analysis tools, improving the efficiency and accuracy of data processing.

Comment 5: To calculate MOS scores, one must take the arithmetic mean of the quality ratings of synthesized speech, given by specific individuals on a scale from 1 to 5. It should be noted that this assessment is not absolute, as it is subjective, so comparing experiments conducted by different groups of people at different times on different data is incorrect. Additionally, I note some difficulties: - With significant discrepancies in phrase length, the sound is heavily distorted; - Style transfer works unstably; - The quality of data used during training critically affects the final result when changing the speaker. How did the authors address these challenges?

Response 5: Thank you for pointing this out. We agree with this comment. As a subjective evaluation method, we acknowledge the existence of the issues you mentioned. However, in this study, the main purpose of using MOS scoring is to compare the relative quality of different models. Therefore, as long as the subjects feel that the synthesized speech of which model is good, they will give a relatively high score. In this study, we randomly selected 30 sentences from the test set and had 20 native Mandarin-speaking students and 10 Dungan international students (who understood Chinese) undergo MOS evaluation. Of course, these participants received training before the formal evaluation. We take the average of all reviewers as the final result. Of course, to address the shortcomings of MOS ratings, we also asked reviewers to rate natural speech as ground truth. From the final results, our proposed Dungan language speech synthesis model based on transfer learning can synthesize more natural speech than other methods. Please find it in Section 3.2.4.

Comment 6: I think it would be interesting to compare the authors' approach with VITS. VITS is a parallel end-to-end TTS system that uses a variational autoencoder (VAE) to connect the acoustic model with the vocoder through a latent (hidden) representation. This approach allows generating high-quality audio recordings by enhancing the expressive features of the network with a mechanism of normalizing flows and adversarial training in the signal's time domain. It also adds the ability to pronounce text with various variations by modeling the uncertainty of the latent state and a stochastic duration predictor.

Response 6: Thank you for pointing this out. We agree with this comment. Numerous breakthroughs have been achieved in TTS based on deep neural networks. We have noticed that some new speech synthesis methods have been proposed in recent years, and your mentioned VITS is one of them. With the widespread use of discrete audio tokens, the research paradigm in language models has shown a profound impact on speech modeling and synthesis. Motivated by recent advancements in auto-regressive (AR) models employing decoder-only architectures for text generation, several studies, such as VALL-E and BASE TTS, apply similar architectures to TTS tasks. These studies demonstrate the remarkable capacity of decoder-only architectures to produce natural-sounding speech. However, these new speech synthesis methods have only begun to be applied in the speech synthesis of low-resource languages. We will further deepen our research, use these new methods to improve the quality of Dungan language speech synthesis, and compare them with the method proposed in this manuscript. We mentioned this in Section Conclusions.

Article is improved

I have formulated the following comments on the previous version of the article:

1. The task of automatic speech synthesis consists of three stages. The first stage is linguistic analysis, which includes text normalization, word segmentation, morphological tagging, grapheme-to-phoneme conversion (G2P), and the extraction of various linguistic features. The second stage involves converting the input sequence of phonemes into a spectrogram – a representation of the signal in the frequency-time domain. The final stage is the reconstruction of the sound wave from the spectrogram, usually using a special algorithm called a vocoder. In my opinion, not all operations of the first stage are fully described by the authors. 2. Recently, the quality of modern adaptive synthesis models has become comparable to real human speech. This has largely been achieved through the use of end-to-end TTS models, which employ data-driven methods based on generative modeling. I think it would be beneficial to compare these with the authors' approach. 3. Modern solutions used in speech technology are based on neural networks and, consequently, require extensive training datasets. For tasks such as speech and emotion recognition, speaker identification, and audio synthesis, datasets with expressive speech are necessary. It is easy to imagine the problems that arise when collecting such data. Firstly, it is necessary to evoke the required emotion in a person, but not all reactions can be induced in simple and natural ways. Secondly, using recordings of professional actors can lead to significant financial costs and artificial emotions. I believe these difficulties fully apply to the Dungan language. I ask the authors to comment on this point. 4. To calculate MOS scores, one must take the arithmetic mean of the quality ratings of synthesized speech, given by specific individuals on a scale from 1 to 5. It should be noted that this assessment is not absolute, as it is subjective, so comparing experiments conducted by different groups of people at different times on different data is incorrect. Additionally, I note some difficulties: - With significant discrepancies in phrase length, the sound is heavily distorted; - Style transfer works unstably; - The quality of data used during training critically affects the final result when changing the speaker. How did the authors address these challenges? 5. I think it would be interesting to compare the authors' approach with VITS. VITS is a parallel end-to-end TTS system that uses a variational autoencoder (VAE) to connect the acoustic model with the vocoder through a latent (hidden) representation. This approach allows generating high-quality audio recordings by enhancing the expressive features of the network with a mechanism of normalizing flows and adversarial training in the signal's time domain. It also adds the ability to pronounce text with various variations by modeling the uncertainty of the latent state and a stochastic duration predictor.

The authors have addressed all my comments. I found their responses quite convincing. I support the publication of the current version of the article. I wish the authors creative success.

Comments 1: T he authors have addressed all my comments. I found their responses quite convincing. I support the publication of the current version of the article. I wish the authors creative success.

Response 1: We are grateful for your acknowledgment of our revision efforts and the insightful comments you offered, which have significantly enhanced the quality of our paper.

Liu, M.; Jiang, R.; Yang, H. Using Transfer Learning to Realize Low Resource Dungan Language Speech Synthesis. Appl. Sci. 2024 , 14 , 6336. https://doi.org/10.3390/app14146336

Liu M, Jiang R, Yang H. Using Transfer Learning to Realize Low Resource Dungan Language Speech Synthesis. Applied Sciences . 2024; 14(14):6336. https://doi.org/10.3390/app14146336

Liu, Mengrui, Rui Jiang, and Hongwu Yang. 2024. "Using Transfer Learning to Realize Low Resource Dungan Language Speech Synthesis" Applied Sciences 14, no. 14: 6336. https://doi.org/10.3390/app14146336

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Trump Likens Migrants To ‘Late Great Hannibal Lecter’ In RNC Speech—A Comparison He’s Used Before

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Former President Donald Trump railed against undocumented migrants, threatened them with mass deportation and compared them with the movie villain Hannibal Lecter again in his headlining speech at the fourth and final day of the Republican National Convention on Thursday.

Former US President Donald Trump railed against undocumented migrants and asylum seekers in his ... [+] speech at the Republican National Convention.

In the portion of his speech focusing on immigration, Trump claimed violent crimes have dropped in Latin American countries because “they’re sending their murderers” to the U.S. and added they are coming from “prisons…mental institutions and insane asylums.”

The former president then asked the RNC audience if they had seen the 1991 film “Silence of the Lambs,” saying the “late, great Hannibal Lecter” who would “love to have you for dinner…That’s insane asylums.”

While comparing undocumented migrants with the cannibalistic movie villain, Trump claimed without evidence that countries like Venezuela and El Salvador are “emptying out their insane asylums” and sending over terrorists “at numbers that we’ve never seen before.”

Key Background

This isn’t the first time Trump has mentioned the fictional cannibal—who has also appeared in several books, TV shows and spin-off films—in his speeches. While speaking at his campaign rallies in May, Trump repeatedly compared migrants to the serial killer character, who he again referred to as “the late, great Hannibal Lecter” and said, “He’s a wonderful man.” The former president would also joke about how Lecter would “have a friend for dinner”—referencing his cannibalism—before saying: “We have people who are being released into our country that we don’t want in our country, and they’re coming in totally unchecked, totally unvetted.” In other speeches, Trump has claimed migrants entering through the southern border were “from mental institutions, insane asylums…you know, insane asylums, that’s ‘Silence of the Lambs’ stuff,” before adding “Anybody know Hannibal Lecter? We don’t want ‘em in this country.”

Surprising Fact

Despite Trump describing the character as the “late, great Hannibal Lecter,” the character survives at the end of the Oscar-winning film. The actor who played Lecter, Anthony Hopkins, is still working in television and cinema at 86 years old.

Chief Critic

In a statement to the New York Times , a Biden campaign spokesperson mocked Trump for mentioning the fictional character: “He has not changed. He has not moderated. He has gotten worse — except now he talks about the ‘late, great, Hannibal Lecter.’”

Further Reading

Trump Calls Fictional Hannibal Lecter ‘A Wonderful Man’ At Rare Campaign Stop During Trial (Forbes)

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When will Republican VP pick JD Vance speak at the RNC?

As the Republican National Convention in Milwaukee stretches into its third day, Ohio Sen. JD Vance is slated to deliver his first address as a vice presidential nominee Wednesday evening.

Vance's speech comes two days after former president Donald Trump announced the junior senator as his VP pick on the first day of the convention. The Ohio senator clinched the nomination over others on Trump's VP shortlist, including Florida Sen. Marko Rubio and North Dakota Gov. Doug Burgum.

"After lengthy deliberation and thought, and considering the tremendous talents of many others, I have decided that the person best suited to assume the position of Vice President of the United States is Senator J.D. Vance of the Great State of Ohio," Trump wrote in a post on Truth Social on Monday.

Elected to the U.S. Senate in 2022, Vance, 39, rose to the political stage off the fame of his book "Hillbilly Elegy," which tells his journey growing up around drug addiction and going to on serve as a U.S. Marine and attend Yale Law School. Vance is likely to hit on his past in his speech.

When will JD Vance speak?

Vance will give his RNC speech Wednesday evening on the second to last day of the convention. Wednesday's theme is "Make American Safe Again," where the focus will be on military and foreign policy. Neither Vance nor Trump have given an official address at the convention, but the former president will close out the convention Thursday with a keynote speech.

Who else is speaking at the RNC today?

Others expected to take the RNC stage on Wednesday include Trump's sons, Donald Trump Jr. and Eric Trump, as well former Fox News host Tucker Carlson.

How can you watch the event and stay up-to-date on convention news?

USA TODAY is streaming the RNC from start to finish, and you can watch it here: