define speech synthesis software

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.

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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

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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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The Ultimate Guide to Speech Synthesis

Table of contents.

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.

Basics of Speech Synthesis

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.

Three Stages of Speech Synthesis

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.

Most Realistic TTS and Best TTS for Android

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.

Best Python Library for Text to Speech

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.

Speech Recognition and Text-to-Speech

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.

Pronunciation of the word “Robot”

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.

Example of a Text-to-Speech Program

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.

Best TTS Engine for Android

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.

Difference Between Concatenative and Unit Selection Synthesizers

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.

Top 8 Speech Synthesis Software or Apps

• 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.
• Previous Understanding Veed: Terms of Service, Commercial Rights, and Safe Usage
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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 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?

• December 23, 2022

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

Sep 28, 2021 1:08:38 pm.

Today, speech synthesis technologies are in demand more than ever. Businesses, film studios, game producers, and video bloggers use speech 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.

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 it 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.

• 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.

• 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.

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.

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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Alex Serdiuk

CEO and Co-founder

Alex founded Respeecher with Dmytro Bielievtsov and Grant Reaber in 2018. Since then the team has been focused on high-fidelity voice cloning. Alex is in charge of Business Development and Strategy. Respeecher technology is already applied in Feature films and TV projects, Video Games, Animation studios, Localization, media agencies, Healthcare, and other areas.

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What is Speech Synthesis?

Speech synthesis, or text-to-speech, is a category of software or hardware that converts text to artificial speech. A text-to-speech system is one that reads text aloud through the computer's sound card or other speech synthesis device. Text that is selected for reading is analyzed by the software, restructured to a phonetic system, and read aloud. The computer looks at each word, calculates its pronunciation then says the word in its context (Cavanaugh, 2003).

How can speech synthesis help your students?

Speech synthesis has a wide range of components that can aid in the reading process. It assists in word decoding for improved reading comprehension (Montali & Lewandowski, 1996). The software gives voice to difficult words with which students struggle by reading either scanned-in documents or imported files (such as eBooks). In word processing, it will read back students' typed text for them to hear what they have written and then make revisions. The software provides a range in options for student control such as tone, pitch, speed of speech, and even gender of speaker. Highlighting features allow the student to highlight a word or passage as it is being read.

Who can benefit from speech synthesis?

According to O'Neill (1999), there are a wide range of users who may benefit from this software, including:

• Students with a reading, learning, and/or attention disorder
• Students who are struggling with reading
• Students who speak English as a second language
• Students with low vision or certain mobility problems

What are some speech synthesis programs?

eReader by CAST

The CAST eReader has the ability to read content from the Internet, word processing files, scanned-in text or typed-in text, and further enhances that text by adding spoken voice, visual highlighting, document navigation, page navigation, type and talk capabilities. eReader is available in both Macintosh and Windows versions.

40 Harvard Mills Square, Suite 3 Wakefield, MA 01880-3233 Tel: 781-245-2212 Fax: 781-245-5212 TTY: 781-245-9320 E-mail:  [email protected]

ReadPlease 2003 This free software can be used as a simple word processor that reads what is typed.

ReadPlease ReadingBar ReadingBar (a toolbar for Internet Explorer) allows users to do much more than they were able to before: have web pages read aloud, create MP3 sound files, magnify web pages, make text-only versions of any web page, dictionary look-up, and even translate web pages to and from other languages. ReadingBar is not limited to reading and recording web pages - it is just as good at reading and recording text you see on your screen from any application. ReadingBar is often used to proofread documents and even to learn other languages.

ReadPlease Corporation 121 Cherry Ridge Road Thunder Bay, ON, Canada - P7G 1A7 Phone: 807-474-7702 Fax: 807-768-1285

Read & Write v.6 Software that provides both text reading and work processing support. Features include: speech, spell checking, homophones support, word prediction, dictionary, word wizard, and teacher's toolkit.

textHELP! Systems Ltd. Enkalon Business Centre, 25 Randalstown Road, Antrim Co. Antrim BT41 4LJ N. Ireland [email protected]

Kurweil 3000 Offers a variety of reading tools to assist students with reading difficulties. Tools include: dual highlighting, tools for decoding, study skills, and writing, test taking capabilities, web access and online books, human sounding speech, bilingual and foreign language benefits, and network access and monitoring.

Kurzweil Educational Systems, Inc. 14 Crosby Drive Bedford, MA 01730-1402 From the USA or Canada: 800-894-5374 From all other countries: 781-276-0600

Max's Sandbox In MaxWrite (the Word interface), students type and then hear "Petey" the parrot read their words. In addition, it is easy to add the student's voice to the document (if you have a microphone for your computer). It is a powerful tool for documenting student writing and reading and could even be used in creating a portfolio of student language skills. In addition, MaxWrite has more than 300 clip art images for students to use, or you can easily have students access your own collection of images (scans, digital photos, or clip art). Student work can be printed to the printer you designate and saved to the folder you determine (even network folders).

Publisher: eWord Development  

Where can you find more information about speech synthesis?

Research Articles

   MacArthur, Charles A. (1998). Word processing with

speech synthesis and word prediction: Effects on the

Descriptive Articles

Center for Applied Special Technology (CAST) Founded in 1984 as the Center for Applied Special Technology, CAST is a not-for-profit organization whose mission is to expand educational opportunities for individuals with disabilities through the development and innovative uses of technology. CAST advances Universal Design for Learning (UDL), producing innovative concepts, educational methods, and effective, inclusive learning technologies based on theoretical and applied research. To achieve this goal, CAST:

• Conducts applied research in UDL,
• Develops and releases products that expand opportunities for learning through UDL,
• Disseminates UDL concepts through public and professional channels.

LD Online LD OnLine is a collaboration between public broadcasting and the learning disabilities community. The site offers a wide range of articles and links to information on assistive technology such as speech synthesis.

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Capterra Glossary

Speech Synthesis

Speech synthesis is the process of creating artificial human speech using a computerized device. These devices are referred to as speech synthesizers or speech computers. There are three phases of the speech synthesis process. During the normalization phase, a speech synthesizer reads a piece of text and uses statistical probability techniques to decide what the most appropriate way to read it aloud would be. The next stage of the process requires the speech synthesizer to use phonemes to generate the sounds necessary to read the piece of text aloud. Next, the speech synthesizer uses short recordings of human speech and sound generation techniques to mimic a human voice and read the piece of text aloud. Businesses in various industries use speech synthesis to create human-like voices for audiobook recordings, video game character voices, and virtual assistant voices.

What Small and Midsize Businesses Need to Know About Speech Synthesis

Small video game development companies with limited budgets often use speech synthesis as a cost-effective way to generate voices for their video game characters. Small publishing companies often use speech synthesis to create audiobooks for their various publications, eliminating the need to pay voice actors to read and record their published works aloud.

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Deep Learning in TTS: Latest Techniques and Tools for Speech Synthesis

Unreal Speech

Unlocking the secrets of deep learning in text-to-speech systems.

In the realm of speech synthesis software, deep learning stands as a revolutionary force, propelling TTS systems into realms of unprecedented realism and functionality. These cutting-edge systems are no longer confined to robotic monotones but now have the capability to convey the intricacies and inflections of human speech with remarkable fidelity. By harnessing the power of advanced neural networks, developers have made significant strides in creating software that can accurately mimic human speech patterns, enabling applications from AI tools for speech to more natural-sounding virtual assistants and chatbots.

With the advent of free TTS software for PC, the technology has become more accessible, fostering innovation in everything from online free unlimited TTS to high-quality, AI-powered voice cloning. This democratization of technology allows for rapid experimentation and deployment, furthering the research and development in speech synthesis. Meanwhile, Google's TTS technology and other online text-to-speech synthesis platforms continue to evolve, drawing on powerful algorithms to provide users with not just speech, but speech that emulates the cadence and emotion of authentic dialogue.

Exploring the Impact of Deep Learning on TTS

Embark on a journey through the pivotal impact of deep learning on text-to-speech (TTS) technologies, where algorithms inspired by the human brain transform text into spoken word with astonishing naturalness. To navigate this landscape, it is essential to grasp the key terminologies that form the cornerstone of TTS advancements. We introduce a glossary designed to enrich your understanding and enhance the dialogue surrounding these evolving technologies.

"A Deep Learning Approaches in Text-to-Speech System: A Systematic Review and Recent Research Perspective"

Published in the esteemed "Multimedia Tools and Applications," the research paper by Yogesh Kumar, Apeksha Koul, and Chamkaur Singh delivers a critical analysis of deep learning (DL) methodologies within TTS frameworks. Dated September 29, 2022, it delves into the DL strategies that have reshaped TTS, suggesting that neural networks are central to the current and future landscape of spoken language technology. The authors, whose affiliations range from academic institutions to potentially private research groups, systematically gather and present data that underscores the evolution from traditional synthesis to AI-driven vocalization.

The paper critically explores key trends such as neural TTS, an advanced subset of TTS that integrates DL to create highly accurate and natural voices. It addresses implementations in interactive applications, highlighting the enhancement of user experiences via conversational agents like chatbots. Furthermore, the analysis extends to how these DL processes are revolutionizing systems to offer refined speech quality across diverse languages and dialects.

A focal point of the study is the evaluation of TTS systems based on quality metrics. Recognition rate, accuracy, and collective TTS scores are dissected to compare and contrast the performance of multiple TTS systems. These metrics underscore the strides made in Indian and non-Indian language systems, reflecting the DL techniques that embody the crux of these improvements. Such insights are invaluable for those engaged in designing TTS solutions that are both innovative and culturally nuanced.

Measuring Success: Quality Metrics in TTS Evaluation

To truly gauge the progression and efficacy of TTS systems, the paper advocates for a standardized approach in using quality metrics. Metrics like recognition rate, which measures a system's ability to understand and replicate text with precision, are critical benchmarks. Accuracy is another touchstone, signaling the system's capability to vocally replicate the intended content without distortion. By systematically reviewing different systems' TTS scores—a quantitative indicator of quality—researchers can effectively strategize future enhancements.

Global Challenges: Insights from Indian and Non-Indian TTS Research

The review provides a cross-cultural view of TTS technology, addressing the challenges faced in creating systems that cater to Indian and non-Indian languages. Each linguistic landscape presents its unique deep learning obstacles and opportunities. Whether through refining phonetic accuracy or overcoming dialect diversity, the paper emphasizes the importance of specialized research and development endeavors to make TTS technology inclusive and globally adaptable.

State-of-the-Art Trends in Speech Synthesis Software

The fast-paced world of speech synthesis is ever-evolving, with deep learning (DL) continually driving advancements in text-to-speech (TTS) systems. State-of-the-art trends in the industry are setting new standards for what TTS can achieve, from creating extraordinarily lifelike voices to facilitating more natural human-computer interaction. Breakthroughs in AI tools for speech are streamlining processes across various sectors, including education, health care, and customer service, proving the versatility and critical necessity of these developments.

Advances in speech synthesis are not just about voice quality but also about the utility and flexibility of these tools. Software updates now often include multi-lingual support, adaptive learning abilities to enhance voice modulation, and the backing of robust frameworks capable of handling vast datasets for nuanced voice generation. As free text-to-speech software for PC becomes sophisticated, more users can access high-quality voice generation for personal and professional uses, signaling a democratization of speech technology tools.

Within the TTS field, one standout trend is the development of open-source projects that invite collaboration and innovation from developers worldwide. By sharing advancements and build-ups, the community collectively pushes the boundaries of what synthetic speech can emulate. Combined with the explosion of cloud-based TTS services, these advancements promise a future where access to high-quality synthetic vocalization is easy, inexpensive, and nearly indistinguishable from human speech.

Technical Quickstart: TTS Development with Programming Code Samples

Python snippets for text to speech synthesis online free.

First, install the gTTS library using pip:

After installation, you can write a script to convert text into speech. Here's a simple Python code snippet:

This code generates an MP3 file from the text "Hello World!" using an English-speaking voice. You can play this MP3 file on any compatible audio software.

Java and JavaScript Techniques for Text to Speech Synthesis

For Java enthusiasts, TTS can be integrated using the FreeTTS library, a wrapper for the Festival TTS engine. However, to keep our quickstart guide concise and up to date, we'll focus on the more common scenario for TTS development: the Web, where JavaScript is the language of choice.

Using the Web Speech API, which is supported in most modern browsers, you can easily implement TTS with the following JavaScript code:

This JavaScript snippet creates an instance of the SpeechSynthesisUtterance object with the text you want to speak and then passes it to the speechSynthesis system of the browser, effectively turning text into audible speech.

Leveraging Free TTS Tools for Enhanced Communication

Unreal Speech is carving out a space as a cost-effective solution in the TTS field, with its API offering up to a 90% slash in costs compared to competitors like Eleven Labs and Play.ht, and up to double the affordability against giants such as Amazon, Microsoft, and Google. Their enterprise plan, including 625M characters for about $4999 a month, positions them as an attractive option for high-volume processing needs - boasting a latency of 0.3 seconds, impressive 99.9% uptime, and capabilities of handling thousands of pages per hour.

From academic research to software engineering, and from game development to educational tools, Unreal Speech's API serves a broad spectrum of users looking for efficient and economical TTS options. With an enterprise-level plan suitable for extensive use, this platform could be especially beneficial for those who need to synthesize large volumes of text into speech regularly. Its scalable pricing model becomes more cost-effective with increased usage, making it ideal for initiatives that might otherwise be curtailed by budget constraints.

For developers, the platform is straightforward to integrate, offering Python, Node.js, and React Native code samples to get started quickly. Whether you are developing real-time apps that require instantaneous audio playback or creating lengthy audio for content consumption, Unreal Speech provides the tools to generate high-quality TTS at impressive speeds. And with the anticipated addition of multilingual voice support, its applications are set only to expand further, providing valuable assets to a diverse range of industries.

Common Questions Re: TTS Techniques and Tools

How can speech synthesis software improve user experience.

Speech synthesis software can significantly enhance user experience by providing natural-sounding voice output. These tools enable more human-like interaction with technology, making it accessible and user-friendly.

What Sets Apart Speech Synthesis from Traditional TTS Methods?

Speech synthesis typically involves advanced algorithms and deep learning to generate speech that mimics human intonation and emotion, whereas traditional TTS methods may rely on more basic concatenation of recorded speech sounds.

How Does the TTS Algorithm Enhance Speech Clarity and Naturalness?

A TTS algorithm enhances speech clarity and naturalness by using machine learning to understand context and apply appropriate inflections, thereby producing more intuitive and seamless synthetic speech.

An Overview of Speech Synthesis Technology

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The Utilization of Speech Synthesis, New Applications

Speech synthesis, also known as text-to-speech, is defined as the artificial or computer generation of human speech. In conjunction with voice recognition , speech synthesis represents one of the foremost means by which written text can be transformed into speech or audio information, whether this is in the context of a voice-enabled service or a mobile application, among many others. For example, the ability of a virtual assistant such as Amazon’s Alexa to respond to questions and commands is made possible by speech recognition and synthesis. With all this being said, many consumers may not know about how speech synthesis works.

Natural language processing

Speech synthesis functions on the basis of two primary concepts, with the first being Natural Language Processing (NLP). NLP represents an interdisciplinary approach to generating interactions between human beings and computers that allow for the creation of machines that can analyze and mimic human speech and written language. To this point, the disciplinary fields of linguistics, artificial intelligence, and computer science have enabled software developers to create various products and services that can imitate human communication, in accordance with large sets of training data and machine learning algorithms that are used to create language models .

As it pertains to speech synthesis, NLP is used to convert raw text into speech, also known as a phonetic transcript. This transcript includes punctuation, numbers, symbols, and abbreviations, in addition to various other elements. Furthermore, NLP will also be used to implement phenomes, or parts of speech, into a speech synthesis software program, much like a young child would need to learn about nouns, verbs, and adjectives in order to speak English in an effective manner. Moreover, NLP will also be used to introduce prosody into the software, such as rates of speech, rhythm, and intonation, as these factors also influence the ways in which human beings communicate with each other.

Digital Signal Processing

The second concept that allows for speech synthesis is Digital Signal Processing (DSP). Put in the simplest of terms, DSP works to turn the phonetic transcript that is created by an NLP algorithm into machine language or speech. This can be achieved in two different ways, which include rule-based and concatenative synthesis. Firstly, rule-based synthesizers imitate human speech through the utilization of parameters such as noise, voice, and frequency levels. These parameters will be tweaked and modified gradually until an artificial speech waveform is created. Despite all of this, rule-based synthesizers will typically generate speech that sounds robotic or unnatural.

Alternatively, concatenative synthesis a created by stringing together multiple files of recorded human speech that have been extracted from a database of speech samples. Due to this fact alone, concatenative synthesizers will produce machine speech that is much more coherent and natural sounding than the speech that is generated by a rule-based synthesizer. However, this also means that concatenative synthesizers will require more data and computational power to generate, as the approach relies on hundreds if not thousands of speech samples to function efficiently. With all this being said, the decision to implement a rule-based or concatenative synthesizer into a speech synthesis program will invariably depend on the manner in which the program will be used.

Speech synthesis and accessibility

In addition to virtual assistants and customer service chatbots, speech synthesis can also be a very useful tool for individuals that have physical or sensory disabilities. For example, an individual who is blind could utilize speech synthesis to gain information from an online website, despite the fact that they cannot physically read the website with their eyes. To this point, many government agencies, as well as private organizations and businesses, have taken steps in recent years to make their online websites and applications accessible to people with disabilities, otherwise known as 508 compliance . As such, speech synthesis provides professionals with another tool that can be used to make content and information more generally accessible.

While many consumers will have undoubtedly come into contact with some form of speech synthesis, be it in the form of a Hollywood movie portrayal or a tangible product or service, the complex processes that allow the technology to operate in a systemic and organized manner are much less well known. Nevertheless, the advent of speech recognition and synthesis has given software developers a means by which to create products, systems, and services that can provide both entertainment and practical assistance to members at all levels of modern-day society.

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• Published: 24 April 2019

Speech synthesis from neural decoding of spoken sentences

• Gopala K. Anumanchipalli 1 , 2   na1 ,
• Josh Chartier 1 , 2 , 3   na1 &
• Edward F. Chang 1 , 2 , 3  

Nature volume  568 ,  pages 493–498 ( 2019 ) Cite this article

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• Brain–machine interface
• Sensorimotor processing

Technology that translates neural activity into speech would be transformative for people who are unable to communicate as a result of neurological impairments. Decoding speech from neural activity is challenging because speaking requires very precise and rapid multi-dimensional control of vocal tract articulators. Here we designed a neural decoder that explicitly leverages kinematic and sound representations encoded in human cortical activity to synthesize audible speech. Recurrent neural networks first decoded directly recorded cortical activity into representations of articulatory movement, and then transformed these representations into speech acoustics. In closed vocabulary tests, listeners could readily identify and transcribe speech synthesized from cortical activity. Intermediate articulatory dynamics enhanced performance even with limited data. Decoded articulatory representations were highly conserved across speakers, enabling a component of the decoder to be transferrable across participants. Furthermore, the decoder could synthesize speech when a participant silently mimed sentences. These findings advance the clinical viability of using speech neuroprosthetic technology to restore spoken communication.

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A neural speech decoding framework leveraging deep learning and speech synthesis

Real-time synthesis of imagined speech processes from minimally invasive recordings of neural activity

A high-performance speech neuroprosthesis

Data availability.

The data that support the findings of this study are available from the corresponding author upon request.

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All code may be freely obtained for non-commercial use by contacting the corresponding author.

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Acknowledgements

We thank M. Leonard, N. Fox and D. Moses for comments on the manuscript and B. Speidel for his help reconstructing MRI images. This work was supported by grants from the NIH (DP2 OD008627 and U01 NS098971-01). E.F.C. is a New York Stem Cell Foundation-Robertson Investigator. This research was also supported by The William K. Bowes Foundation, the Howard Hughes Medical Institute, The New York Stem Cell Foundation and The Shurl and Kay Curci Foundation.

Reviewer information

Nature thanks David Poeppel and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

These authors contributed equally: Gopala K. Anumanchipalli, Josh Chartier

Authors and Affiliations

Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA

Gopala K. Anumanchipalli, Josh Chartier & Edward F. Chang

Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA

University of California Berkeley and University of California San Francisco Joint Program in Bioengineering, Berkeley, CA, USA

Josh Chartier & Edward F. Chang

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Contributions

G.K.A., J.C. and E.F.C. conceived the study; G.K.A. inferred articulatory kinematics; G.K.A. and J.C. designed the decoder; J.C. performed decoder analyses; G.K.A., E.F.C. and J.C. collected data and prepared the manuscript; E.F.C. supervised the project.

Corresponding author

Correspondence to Edward F. Chang .

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The authors declare no competing interests.

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Extended data figures and tables

Extended data fig. 1 median original and decoded spectrograms..

a , b , Median spectrograms, time-locked to the acoustic onset of phonemes from original ( a ) and decoded ( b ) audio (/i/, n  = 112; /z/, n  = 115; /p/, n  = 69, /ae/, n  = 86). These phonemes represent the diversity of spectral features. Original and decoded median phoneme spectrograms were well-correlated (Pearson’s r  > 0.9 for all phonemes, P  = 1 × 10 −18 ).

Extended Data Fig. 2 Transcription WER for individual trials.

a , b , WERs for individually transcribed trials for pools with a size of 25 ( a ) or 50 ( b ) words. Listeners transcribed synthesized sentences by selecting words from a defined pool of words. Word pools included correct words found in the synthesized sentence and random words from the test set. One trial is one transcription of one listener of one synthesized sentence.

Extended Data Fig. 3 Electrode array locations for participants.

MRI reconstructions of participants’ brains with overlay of electrocorticographic electrode (ECoG) array locations. P1–5, participants 1–5.

Extended Data Fig. 4 Decoding performance of kinematic and spectral features.

Data from participant 1. a , Correlations of all 33 decoded articulatory kinematic features with ground-truth ( n  = 101 sentences). EMA features represent x and y coordinate traces of articulators (lips, jaw and three points of the tongue) along the midsagittal plane of the vocal tract. Manner features represent complementary kinematic features to EMA that further describe acoustically consequential movements. b , Correlations of all 32 decoded spectral features with ground-truth ( n  = 101 sentences). MFCC features are 25 mel-frequency cepstral coefficients that describe power in perceptually relevant frequency bands. Synthesis features describe glottal excitation weights necessary for speech synthesis. Box plots as described in Fig.  2 .

Extended Data Fig. 5 Comparison of cumulative variance explained in kinematic and acoustic state–spaces.

For each representation of speech—kinematics and acoustics—a principal components analysis was computed and the explained variance for each additional principal component was cumulatively summed. Kinematic and acoustic representations had 33 and 32 features, respectively.

Extended Data Fig. 6 Decoded phoneme acoustic similarity matrix.

Acoustic similarity matrix compares acoustic properties of decoded phonemes and originally spoken phonemes. Similarity is computed by first estimating a Gaussian kernel density for each phoneme (both decoded and original) and then computing the Kullback–Leibler (KL) divergence between a pair of decoded and original phoneme distributions. Each row compares the acoustic properties of a decoded phoneme with originally spoken phonemes (columns). Hierarchical clustering was performed on the resulting similarity matrix. Data from participant 1.

Extended Data Fig. 7 Ground-truth acoustic similarity matrix.

The acoustic properties of ground-truth spoken phonemes are compared with one another. Similarity is computed by first estimating a Gaussian kernel density for each phoneme and then computing the Kullback–Leibler divergence between a pair of a phoneme distributions. Each row compares the acoustic properties of two ground-truth spoken phonemes. Hierarchical clustering was performed on the resulting similarity matrix. Data from participant 1.

Extended Data Fig. 8 Comparison between decoding novel and repeated sentences.

a , b , Comparison metrics included spectral distortion ( a ) and the correlation between decoded and original spectral features ( b ). Decoder performance for these two types of sentences was compared and no significant difference was found ( P  = 0.36 ( a ) and P  = 0.75 ( b ), n  = 51 sentences, Wilcoxon signed-rank test). A novel sentence consists of words and/or a word sequence not present in the training data. A repeated sentence is a sentence that has at least one matching word sequence in the training data, although with a unique production. Comparison was performed on participant 1 and the evaluated sentences were the same across both cases with two decoders trained on differing datasets to either exclude or include unique repeats of sentences in the test set. ns, not significant; P  > 0.05. Box plots as described in Fig.  2 .

Extended Data Fig. 9 Kinematic state–space trajectories for phoneme-specific vowel–consonant transitions.

Average trajectories of principal components 1 (PC1) and 2 (PC2) for transitions from either a consonant or a vowel to specific phonemes. Trajectories are 500 ms and centred at transition between phonemes. a , Consonant to corner vowels ( n  = 1,387, 1,964, 2,259, 894, respectively, for aa, ae, iy and uw). PC1 shows separation of all corner vowels and PC2 delineates between front vowels (iy, ae) and back vowels (uw, aa). b , Vowel to unvoiced plosives ( n  = 2,071, 4,107 and 1,441, respectively, for k, p and t). PC1 was more selective for velar constriction (k) and PC2 for bilabial constriction (p). c , Vowel to alveolars ( n  = 3,919, 3,010 and 4,107, respectively, for n, s and t). PC1 shows separation by manner of articulation (nasal, plosive or fricative) whereas PC2 is less discriminative. d , PC1 and PC2 show little, if any, delineation between voiced and unvoiced alveolar fricatives ( n  = 3,010 and 1,855, respectively, for s and z).

Supplementary information

Supplementary information.

This file contains: a) Place-manner tuples used to augment EMA trajectories; b) Sentences used in listening tests Original Source: MOCHA-TIMIT20 dataset; c) Class sizes for the listening tests; d) Transcription interface for the intelligibility assessment; and e) Number of listeners used for intelligibility assessments.

Reporting Summary

Supplemental video 1: examples of decoded kinematics and synthesized speech production.

The video presents examples of synthesized audio from neural recordings of spoken sentences. In each example, electrode activity corresponding to a sentence is displayed (top). Next, simultaneous decoding of kinematics and acoustics are visually and audible presented. Decoded articulatory movements are displayed (middle left) as the synthesized speech spectrogram unfolds. Following the decoding, the original audio, as spoken by the patient during neural recording, is played. Lastly, the decoded movements and synthesized speech is once again presented. This format is repeated for a total of five examples (from participants P1 and P2). On the last example, kinematics and audio are also decoded and synthesized for silently mimed speech.

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Anumanchipalli, G.K., Chartier, J. & Chang, E.F. Speech synthesis from neural decoding of spoken sentences. Nature 568 , 493–498 (2019). https://doi.org/10.1038/s41586-019-1119-1

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Received : 29 October 2018

Accepted : 21 March 2019

Published : 24 April 2019

Issue Date : 25 April 2019

DOI : https://doi.org/10.1038/s41586-019-1119-1

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Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text, is a capability that enables a program to process human speech into a written format.

While speech recognition is commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user’s voice.

IBM has had a prominent role within speech recognition since its inception, releasing of “Shoebox” in 1962. This machine had the ability to recognize 16 different words, advancing the initial work from Bell Labs from the 1950s. However, IBM didn’t stop there, but continued to innovate over the years, launching VoiceType Simply Speaking application in 1996. This speech recognition software had a 42,000-word vocabulary, supported English and Spanish, and included a spelling dictionary of 100,000 words.

While speech technology had a limited vocabulary in the early days, it is utilized in a wide number of industries today, such as automotive, technology, and healthcare. Its adoption has only continued to accelerate in recent years due to advancements in deep learning and big data.  Research  (link resides outside ibm.com) shows that this market is expected to be worth USD 24.9 billion by 2025.

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Many speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning . They integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Ideally, they learn as they go — evolving responses with each interaction.

The best kind of systems also allow organizations to customize and adapt the technology to their specific requirements — everything from language and nuances of speech to brand recognition. For example:

• Language weighting: Improve precision by weighting specific words that are spoken frequently (such as product names or industry jargon), beyond terms already in the base vocabulary.
• Speaker labeling: Output a transcription that cites or tags each speaker’s contributions to a multi-participant conversation.
• Acoustics training: Attend to the acoustical side of the business. Train the system to adapt to an acoustic environment (like the ambient noise in a call center) and speaker styles (like voice pitch, volume and pace).
• Profanity filtering: Use filters to identify certain words or phrases and sanitize speech output.

Meanwhile, speech recognition continues to advance. Companies, like IBM, are making inroads in several areas, the better to improve human and machine interaction.

The vagaries of human speech have made development challenging. It’s considered to be one of the most complex areas of computer science – involving linguistics, mathematics and statistics. Speech recognizers are made up of a few components, such as the speech input, feature extraction, feature vectors, a decoder, and a word output. The decoder leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output.

Speech recognition technology is evaluated on its accuracy rate, i.e. word error rate (WER), and speed. A number of factors can impact word error rate, such as pronunciation, accent, pitch, volume, and background noise. Reaching human parity – meaning an error rate on par with that of two humans speaking – has long been the goal of speech recognition systems. Research from Lippmann (link resides outside ibm.com) estimates the word error rate to be around 4 percent, but it’s been difficult to replicate the results from this paper.

Various algorithms and computation techniques are used to recognize speech into text and improve the accuracy of transcription. Below are brief explanations of some of the most commonly used methods:

• Natural language processing (NLP): While NLP isn’t necessarily a specific algorithm used in speech recognition, it is the area of artificial intelligence which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting. 
• Hidden markov models (HMM): Hidden Markov Models build on the Markov chain model, which stipulates that the probability of a given state hinges on the current state, not its prior states. While a Markov chain model is useful for observable events, such as text inputs, hidden markov models allow us to incorporate hidden events, such as part-of-speech tags, into a probabilistic model. They are utilized as sequence models within speech recognition, assigning labels to each unit—i.e. words, syllables, sentences, etc.—in the sequence. These labels create a mapping with the provided input, allowing it to determine the most appropriate label sequence.
• N-grams: This is the simplest type of language model (LM), which assigns probabilities to sentences or phrases. An N-gram is sequence of N-words. For example, “order the pizza” is a trigram or 3-gram and “please order the pizza” is a 4-gram. Grammar and the probability of certain word sequences are used to improve recognition and accuracy.
• Neural networks: Primarily leveraged for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold) and an output. If that output value exceeds a given threshold, it “fires” or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent.  While neural networks tend to be more accurate and can accept more data, this comes at a performance efficiency cost as they tend to be slower to train compared to traditional language models.
• Speaker Diarization (SD): Speaker diarization algorithms identify and segment speech by speaker identity. This helps programs better distinguish individuals in a conversation and is frequently applied at call centers distinguishing customers and sales agents.

A wide number of industries are utilizing different applications of speech technology today, helping businesses and consumers save time and even lives. Some examples include:

Automotive: Speech recognizers improves driver safety by enabling voice-activated navigation systems and search capabilities in car radios.

Technology: Virtual agents are increasingly becoming integrated within our daily lives, particularly on our mobile devices. We use voice commands to access them through our smartphones, such as through Google Assistant or Apple’s Siri, for tasks, such as voice search, or through our speakers, via Amazon’s Alexa or Microsoft’s Cortana, to play music. They’ll only continue to integrate into the everyday products that we use, fueling the “Internet of Things” movement.

Healthcare: Doctors and nurses leverage dictation applications to capture and log patient diagnoses and treatment notes.

Sales: Speech recognition technology has a couple of applications in sales. It can help a call center transcribe thousands of phone calls between customers and agents to identify common call patterns and issues. AI chatbots can also talk to people via a webpage, answering common queries and solving basic requests without needing to wait for a contact center agent to be available. It both instances speech recognition systems help reduce time to resolution for consumer issues.

Security: As technology integrates into our daily lives, security protocols are an increasing priority. Voice-based authentication adds a viable level of security.

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speech synthesis

[ speech sin -th uh -sis ]

• the production of computer-generated audio output that resembles human speech, such as the audio generated by screen readers and other text-to-speech software, by virtual assistants and GPS apps, and by assistive technologies that create synthetic speech to vocalize for people with certain disabilities or serious speech impairment.

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Word history and origins.

Origin of speech synthesis 1