Text-to-speech (TTS) technology is the process of converting written text into spoken audio. It has many applications, such as accessibility, education, entertainment, and communication. TTS technology has evolved significantly over the years, from simple synthesized voices that sound robotic and unnatural, to advanced natural language processing (NLP) systems that can produce human-like speech with emotions, accents, and intonation.
In this article, we will explore the history and development of TTS technology, the challenges and opportunities it faces, and the future directions it may take.
Milestones in the History Of Text-to-Speech Technology
Below is a quick summary of the development of speech synthesis technology and the milestones in the history of text-to-speech.
| Year | Event |
|---|---|
| 1700s | A German-Danish scientist Christian Kratzenstein creates acoustic resonators that mimic the human voice. |
| 1952 | AUDREY, the first speech recognition system that recognized spoken numbers, was developed by Bell Laboratories. |
| 1962 | Shoebox, a system that recognized numbers and simple math terms, was developed by IBM. |
| 1968 | Noriko Umeda invents text-to-speech for English at the Electrotechnical Laboratory in Japan. |
| 1970s | Development of the first articulatory synthesizer based on the human vocal tract. |
| 1976 | HARPY, a system that recognized sentences from a vocabulary of 1,011 words using Hidden Markov Models, was developed by Carnegie Mellon University. |
| 1980s | Speech synthesis enters the video game world with the release of Stratovox. Steve Jobs creates NeXT, which later merges with Apple. |
| 1984 | Kurzweil Applied Intelligence released the first commercially available speech recognition software for personal computers. |
| 1990s | Improvements in synthesized speech lead to softer consonants and more natural-sounding voices. Microsoft releases Narrator, a screen reader solution included in Windows. |
| 1990 | Dragon Dictate, the first continuous speech recognition software that allowed users to speak naturally without pauses between words, was released by Dragon Systems. |
| 1996 | Bell Labs introduced AT&T Natural Voices, a text to speech system that used neural networks to generate natural-sounding speech. |
| 2000s | Developers face challenges in creating agreed-upon standards for synthesized speech. |
| 2001 | Microsoft introduced Speech Application Programming Interface (SAPI) 5.0, a standard interface for developing speech applications on Windows platforms. |
| 2006 | Google launched Google Voice Search, a service that allowed users to search the web using voice commands on their mobile phones. |
| 2011 | Apple introduced Siri, a voice-activated personal assistant that used natural language processing and machine learning to answer questions and perform tasks. |
| 2014 | Amazon launched Alexa, a cloud-based voice service that powered smart speakers and other devices with voice interaction capabilities. |
| 2016 | WaveNet, a deep neural network-based model for speech synthesis that generated raw audio waveforms, was developed by DeepMind. |
| 2018 | Baidu introduced Deep Voice 3, a neural network-based model for text to speech that could clone a human voice with only a few minutes of audio data. |
| 2020 | OpenAI introduced Jukebox, a neural network-based model for music generation that could produce songs with lyrics and vocals in various genres and styles. |
| Future | Focus on creating a model of the brain to better understand speech data. Emphasis on understanding the role of emotion in speech and creating AI voices indistinguishable from humans. |
Now let’s go more deeply into the history of text to speech technology.
Historical Development of TTS
Early origins of TTS technology and its initial applications
The early origins of TTS technology can be traced back to the 18th century, when some scientists built models of the human vocal tract that could produce vowel sounds. The first electronic speech synthesizer was invented by Homer Dudley in 1939, and it used a keyboard and a foot pedal to control the pitch and duration of speech sounds.
The initial applications of TTS technology were mainly for accessibility purposes, such as helping visually impaired people with visual impairments or reading disabilities to access written text Later, TTS technology was also used for entertainment, education, and communication purposes, such as creating voice robots, audiobooks, and voice assistants.
The limitations of early TTS systems.
Some of the limitations of early TTS systems were:
- Robotic voices: Early TTS systems used rule-based technologies such as formant synthesis and articulatory synthesis, which achieved a similar result through slightly different strategies. Pioneering researchers recorded a speaker and extracted acoustic features from that recorded speech—formants, defining qualities of speech sounds, in formant synthesis; and articulatory parameters, such as tongue position and lip shape, in articulatory synthesis. These features were then used to synthesize speech sounds from scratch, using mathematical models of the vocal tract and other components of speech production. However, these methods often produced unnatural sounding speech that lacked the prosody, intonation, and variability of human speech.
- Lack of naturalness: Another limitation of early TTS systems was their difficulty with producing natural sounding speech that matched the context, emotion, and intention of the speaker. Early TTS systems relied on fixed rules and algorithms to generate speech, which did not account for the nuances and variations of human language and communication. For example, early TTS systems could not adjust their tone, pitch, or speed according to the mood or attitude of the speaker or the listener. They also could not handle complex linguistic phenomena such as sarcasm, irony, humor, or idioms.
- Pronunciation errors: A third limitation of early TTS systems was their inability to pronounce words correctly in different languages, accents, or dialects. Early TTS systems used text-to-phoneme conversion to map written words to their corresponding speech sounds. However, this process was often inaccurate or incomplete, especially for words that had multiple pronunciations or irregular spellings. Moreover, early TTS systems did not have access to large and diverse databases of speech samples that could cover all the variations and nuances of human speech across different regions and cultures. As a result, early TTS systems often mispronounced words or phrases that were unfamiliar or uncommon to them
The principles behind early TTS models
The principles behind early TTS models, such as formant synthesis and concatenative synthesis, are:
- Formant synthesis: This method uses mathematical models of the vocal tract and other components of speech production to synthesize speech sounds from scratch1 It relies on extracting acoustic features, such as formants, from recorded speech and using them to control the parameters of the models2 Formant synthesis can produce speech in any language or accent, but it often sounds robotic and unnatural3
- Concatenative synthesis: This method uses pre-recorded speech units, such as phones, diphones, or syllables, and concatenates them to produce speech1 It relies on finding the best matching units for a given text and smoothing the transitions between them2 Concatenative synthesis can produce natural sounding speech, but it requires a large and diverse database of speech samples and it cannot handle out-of-vocabulary words or novel accents
Advancements in TTS Technology
Synthetic Voices and Prosody
Development of synthetic voices and their impact on TTS.
The development of synthetic voices and their impact on TTS are:
- Synthetic voices: Synthetic voices are artificial voices that are created by speech synthesis applications, such as text-to-speech (TTS) systems, that convert text or other symbolic representations into speech. Voices synthesis can be used for various purposes, such as accessibility, education, entertainment, and communication.
- Development: The development of synthetic voices has gone through several stages, from rule-based methods such as formant synthesis and concatenative synthesis, to data-driven methods such as statistical parametric synthesis and neural network-based synthesis. Rule-based methods use mathematical models and pre-recorded speech units to generate speech sounds from scratch or by concatenation. Data-driven methods use machine learning algorithms and large-scale speech corpora to learn the mapping between text and speech features and generate speech by sampling or optimization.
- Impact: The impact of synthetic voices on TTS is that they have improved the quality, naturalness, and diversity of synthesized speech over time. Synthetic voices can now produce speech that is indistinguishable from human speech in some cases, and can also adapt to different languages, accents, styles, and emotions. Synthetic voices can also enable new applications and scenarios for TTS, such as voice cloning, voice conversion, voice impersonation, and voice watermarking. However, synthetic voices also pose some challenges and risks for TTS, such as ethical issues, social implications, and potential misuse of deepfakes and misleading content
Importance of prosody in creating natural-sounding speech.
The importance of prosody (intonation, rhythm, and stress) in creating natural-sounding speech is:
- Prosody is the pattern of variation in pitch, loudness, and duration of speech sounds that conveys information about the structure, meaning, and emotion of an utterance. Prosody is an essential aspect of human speech that affects how we perceive and understand spoken language.
- Prosody modeling is the process of adding the appropriate intonation, stress, and rhythm to the voice output, depending on the context and meaning of the text3 Prosody modeling is crucial for creating natural-sounding TTS that conveys the right feeling and emotion in the speech3 This technology involves analyzing the linguistic and acoustic features of the text and applying the appropriate prosodic rules and patterns2
- Prosody impact is the effect of prosody on the quality, naturalness, and expressiveness of synthesized speech. Prosody impact can improve the intelligibility, clarity, and fluency of speech, as well as the listener’s engagement, attention, and satisfaction2 Prosody impact can also enhance the communication of emotions, attitudes, intentions, and personalities in speech, making it more human-like and realistic
Techniques used to improve prosody in TTS systems
Some of the techniques used to improve prosody in TTS systems are:
- Prosody prediction: This technique involves predicting the prosodic features, such as pitch, duration, and energy, from the input text or other linguistic features1 Prosody prediction can be done using rule-based methods, such as ToBI annotation and Fujisaki model, or data-driven methods, such as decision trees, hidden Markov models, and neural networks. Prosody prediction can improve the intelligibility and naturalness of synthesized speech by adding the appropriate stress, intonation, and rhythm.
- Prosody modeling: This technique involves modeling the prosodic structure and patterns of natural speech and applying them to the voice output. Prosody modeling can be done using rule-based methods, such as superpositional model and target approximation model, or data-driven methods, such as statistical parametric synthesis and neural network-based synthesis. Prosody modeling can improve the quality and expressiveness of synthesized speech by capturing the linguistic and acoustic variations of prosody.
- Prosody control: This technique involves modifying or incorporating the desired prosody at the finer level by controlling the fundamental frequency and the phone duration. Prosody control can be done using rule-based methods, such as pitch scaling and duration scaling, or data-driven methods, such as style tokens and global style tokens3 Prosody control can improve the diversity and adaptability of synthesized speech by enabling different languages, accents, styles, and emotions.
Neural Network-based Models
Emergence of neural network-based models in TTS technology.
The emergence of neural network-based models in TTS technology is:
- Neural network-based models: Neural network-based models are machine learning models that use artificial neural networks to learn the mapping between text and speech features and generate speech by sampling or optimization. Neural network-based models can overcome some of the limitations of rule-based and data-driven methods, such as unnaturalness, lack of diversity, and pronunciation errors.
- Emergence: The emergence of neural network-based models in TTS technology can be attributed to the development of deep learning and artificial intelligence, as well as the availability of large-scale speech corpora and computational resources. The first neural network-based model for TTS was proposed by Zen et al. in 2009, which used a deep neural network (DNN) to predict acoustic features from linguistic features. Since then, various neural network architectures and techniques have been applied to TTS, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), attention mechanisms, generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers.
- Impact: The impact of neural network-based models on TTS technology is that they have achieved state-of-the-art performance in terms of quality, naturalness, and diversity of synthesized speech. Neural network-based models can produce speech that is indistinguishable from human speech in some cases, and can also adapt to different languages, accents, styles, and emotions. Neural network-based models can also enable new applications and scenarios for TTS, such as voice cloning, voice conversion, voice impersonation, and voice watermarking. However, neural network-based models also pose some challenges and risks for TTS, such as data efficiency, interpretability, robustness, and potential misuse of deepfakes and misleading content.
Advantages of neural networks over traditional rule-based approaches.
Some of the advantages of neural networks over rule-based approaches are:
- Data-driven learning: Neural networks can learn the mapping between text and speech features from large-scale speech corpora, without relying on hand-crafted rules or pre-recorded speech units. This makes them more flexible and adaptable to different languages, accents, styles, and emotions.
- End-to-end generation: Neural networks can generate speech directly from text, without intermediate steps such as text analysis, acoustic modeling, and vocoding. This reduces the complexity and error propagation of the synthesis pipeline.
- Naturalness and diversity: Neural networks can produce speech that is more natural and diverse than rule-based approaches, by capturing the linguistic and acoustic variations of prosody and voice quality. Neural networks can also enable new applications and scenarios for TTS, such as voice cloning, voice conversion, voice impersonation, and voice watermarking
Components of neural TTS models
The components of neural TTS models are:
- Text processing: This component involves analyzing the input text and converting it into a sequence of linguistic features, such as phonemes, syllables, words, or characters. Text processing can also include adding punctuation, capitalization, normalization, and other text preprocessing steps. Text processing can be done using rule-based methods, such as text analysis grammars and lexicons, or data-driven methods, such as neural networks and transformers.
- Acoustic modeling: This component involves predicting the acoustic features, such as pitch, duration, and energy, from the linguistic features. Acoustic modeling can also include modeling the prosodic structure and patterns of natural speech and applying them to the voice output. Acoustic modeling can be done using rule-based methods, such as superpositional model and target approximation model, or data-driven methods, such as neural networks and transformers.
- Vocoding: This component involves converting the acoustic features into a continuous audio signal. Vocoding can also include modifying or incorporating the desired voice quality and timbre at the finer level by controlling the fundamental frequency and the phone duration. Vocoding can be done using rule-based methods, such as source-filter model and waveform concatenation, or data-driven methods, such as neural networks and transformers
WaveNet and SampleRNN
Exploration of the revolutionary WaveNet model and its contribution to TTS.
The WaveNet model and its contribution to TTS are:
- WaveNet model: WaveNet is a generative model of raw audio waveforms that uses a deep convolutional neural network with dilated causal convolutions. WaveNet directly models the probability distribution of each audio sample conditioned on all previous samples, using a softmax output layer. WaveNet can generate speech by sampling from this distribution, or by conditioning on additional inputs such as text or speaker identity.
- Contribution to TTS: WaveNet has significantly improved the quality, naturalness, and diversity of synthesized speech compared to previous methods. WaveNet can produce speech that sounds more human-like and realistic, and can also adapt to different languages, accents, styles, and emotions. WaveNet has inspired many subsequent neural network-based models for TTS, such as Tacotron, Deep Voice, and Transformer TTS3 WaveNet has also enabled new applications and scenarios for TTS, such as voice cloning, voice conversion, voice impersonation, and voice watermarking
Ability of WaveNet to generate high-quality, human-like speech through deep generative modeling.
The ability of WaveNet to generate high-quality, human-like speech through deep generative modeling is:
- Deep generative modeling: WaveNet is a deep generative model of raw audio waveforms that uses a deep convolutional neural network with dilated causal convolutions. WaveNet directly models the probability distribution of each audio sample conditioned on all previous samples, using a softmax output layer. WaveNet can generate speech by sampling from this distribution, or by conditioning on additional inputs such as text or speaker identity.
- High-quality speech: WaveNet can produce speech that sounds more natural and realistic than previous methods, by capturing the linguistic and acoustic variations of prosody and voice quality. WaveNet can also adapt to different languages, accents, styles, and emotions. WaveNet has significantly improved the quality of synthesized speech compared to previous methods, reducing the gap with human performance by over 50%.
- Human-like speech: WaveNet can generate speech that mimics any human voice, by directly modeling voice after recordings of human voice over actors. Instead of synthesizing sounds, it is emulating a real person. WaveNet can also enable new applications and scenarios for TTS, such as voice cloning, voice conversion, voice impersonation, and voice watermarking
Introduction of SampleRNN as an alternative approach to generate speech with improved efficiency.
The introduction of SampleRNN as an alternative approach to generate speech with improved efficiency is:
- SampleRNN: SampleRNN is an autoregressive generative model of raw audio waveforms that uses a hierarchical structure of deep recurrent neural networks (RNNs) to model dependencies in the sample sequence. SampleRNN can generate speech by sampling from the conditional distribution of each audio sample given all previous samples and additional inputs such as text or speaker identity.
- Alternative approach: SampleRNN is an alternative approach to WaveNet, which uses a deep convolutional neural network with dilated causal convolutions to generate speech. SampleRNN has different modules operating at different clock-rates, which allows more flexibility in allocating computational resources and modeling different levels of abstraction.
- Improved efficiency: SampleRNN can generate speech with improved efficiency compared to WaveNet, as it has lower computational complexity and memory requirements. SampleRNN can also leverage parallelism and optimization techniques such as teacher forcing and scheduled sampling to speed up training and inference.
Transfer Learning and Multilingual TTS
Advancements in transfer learning techniques for TTS
The advancements in transfer learning techniques for TTS are:
Transfer learning: Transfer learning is a machine learning technique that leverages the knowledge of a pre-trained model for a new task or domain1 Transfer learning can reduce the data requirement and training time for adapting TTS models for a new voice, using only a few minutes of speech data.
Advancements: Some of the advancements in transfer learning techniques for TTS are:
- Fine-tuning single-speaker TTS models: This technique involves fine-tuning high quality single-speaker TTS models for a new speaker, using only a few minutes of speech data. This technique can yield comparable performance to a model trained from scratch on more than 27 hours of data for both male and female target speakers.
- Adapting multi-speaker TTS models: This technique involves adapting pre-trained multi-speaker TTS models for a new voice, using a few minutes of speech data of the new speaker. This technique can either condition the pre-trained model directly on the derived speaker embedding of the new speaker, or fine-tune the model on the new speaker’s data.
- Exploring low resource emotional TTS: This technique involves exploring transfer learning methods for low resource emotional TTS, using a small amount of emotional speech data. This technique can improve the naturalness and expressiveness of synthesized speech by capturing the emotion and style of the target speaker.
Explanation of how transfer learning enables training TTS models in multiple languages with limited data.
How transfer learning enables training TTS models in multiple languages with limited data is:
- Multiple languages: Transfer learning can enable training TTS models in multiple languages with limited data by using cross-lingual or multilingual transfer learning methods. Cross-lingual transfer learning involves fine-tuning a pre-trained TTS model from a high-resource language to a low-resource language, using a small amount of target language data. Multilingual transfer learning involves adapting a pre-trained multi-speaker TTS model to a new language, using a joint multilingual dataset of low-resource languages.
- Limited data: Transfer learning can overcome the data scarcity problem for low-resource languages by using data augmentation and partial network-based transfer learning techniques. Data augmentation involves generating synthetic speech data from the original data by applying various transformations, such as pitch shifting, speed perturbation, and noise addition. Partial network-based transfer learning involves transferring only some layers or modules of the pre-trained model to the new model, while freezing or discarding the rest.
Benefits and challenges of developing multilingual TTS systems
Some of the benefits and challenges of developing multilingual TTS systems are:
- Benefits: Multilingual TTS systems can provide speech synthesis for multiple languages using a single model, which can reduce the data requirement and training time for low-resource languages. Multilingual TTS systems can also improve the quality, naturalness, and diversity of synthesized speech by capturing the linguistic and acoustic variations of different languages. Multilingual TTS systems can also enable new applications and scenarios for TTS, such as cross-lingual synthesis, voice cloning, voice conversion, voice impersonation, and voice watermarking.
- Challenges: Multilingual Text-to-speech systems face several challenges, such as finding a suitable representation for multiple languages, such as the International Phonetic Alphabet (IPA) or graphemes. Multilingual TTS systems also need to deal with the trade-off between language-specific and language-independent modeling, as well as the balance between data quantity and quality for different languages. Multilingual TTS systems also need to address the issues of speaker identity, speaking style, and emotion across different languages.
Challenges and Future Directions
Ethical Considerations
Some of the ethical concerns related to TTS are:
- Voice cloning: Voice cloning is the process of creating a synthetic voice that mimics a specific human voice, using a small amount of speech data from the target speaker. Voice cloning can have positive applications, such as restoring the voice of people who lost their ability to speak due to illness or injury, or preserving the voice of historical figures or celebrities. However, voice cloning can also have negative implications, such as violating the privacy and consent of the target speaker, or creating fake or misleading content that can harm the reputation or credibility of the target speaker.
- Deepfakes: Deepfakes are synthetic media that combine and superimpose existing images and videos onto source images or videos using deep learning techniques. Deepfakes can create realistic and convincing videos or audio clips that show people saying or doing things that they never said or did. Deepfakes can have malicious applications, such as spreading misinformation, propaganda, or defamation, or manipulating public opinion, behavior, or emotions.
- Bias and discrimination: Bias and discrimination are the unfair or prejudicial treatment of people or groups based on characteristics such as race, gender, age, or religion. Bias and discrimination can affect Text-to-speech systems in various ways, such as the selection of languages, accents, styles, and emotions for speech synthesis, or the representation and inclusion of diverse voices and identities in speech data and models. Bias and discrimination can have harmful consequences, such as reinforcing stereotypes, marginalizing minorities, or excluding certain groups from accessing information or services.
That leads us to the importance of responsible use of TTS technology and potential regulations:
- Responsible use: Responsible use of TTS technology is the ethical and legal use of TTS technology that respects the rights, privacy, and consent of voice talent and voice users, and that prevents or minimizes the harm or misuse of synthetic voices. Responsible use of TTS technology requires the engagement and collaboration of stakeholders across the whole technology value chain, from the design and development to the sale and end use of TTS products and services. Responsible use of TTS technology also requires the adoption of best practices and guidelines for ethical decision-making, risk assessment, and transparency and accountability.
- Potential regulations: Potential regulations for TTS technology are the laws and policies that govern the development, deployment, and use of TTS technology, and that protect the interests and rights of voice talent and voice users. Potential regulations for TTS technology may include:
- Data protection and privacy laws: These laws regulate the collection, processing, storage, and sharing of personal data, such as voice recordings or voice models, and require the consent of data subjects and the compliance of data controllers and processors.
- Intellectual property and copyright laws: These laws protect the ownership and rights of voice talent over their voice recordings or voice models, and prevent the unauthorized use or reproduction of their voice by others.
- Anti-fraud and anti-defamation laws: These laws prohibit the creation or dissemination of false or misleading content using synthetic voices, such as deepfakes or voice phishing, that can harm the reputation or credibility of voice talent or voice users.
Real-Time TTS and Low Latency
Some of the challenges in achieving real-time TTS and low latency are:
- Computational complexity: TTS models, especially neural network-based models, have high computational complexity and memory requirements, as they need to process large amounts of text and speech data and generate high-quality audio samples. This can limit the speed and efficiency of TTS models, especially for long-form content or large-scale applications.
- Network congestion: TTS models, especially cloud-based models, rely on network connectivity and bandwidth to deliver speech output to users. However, network congestion can cause delays, packet losses, or jitter in the transmission of speech data, which can degrade the quality and naturalness of synthesized speech.
- User experience: TTS models, especially for real-time communication applications, need to provide a seamless and interactive user experience that matches the expectations and preferences of users. However, user experience can be affected by various factors, such as the latency, reliability, and diversity of synthesized speech, as well as the voice quality, style, and emotion of synthetic voices.
That brings us to the importance of reducing inference time for TTS applications:
- Real-time performance: Reducing inference time for TTS applications can enable real-time speech synthesis, which is a requirement for many practical applications such as digital assistants, mobile phones, embedded devices, etc. Real-time speech synthesis systems can provide a seamless and interactive user experience that matches the expectations and preferences of users.
- Resource efficiency: Reducing inference time for TTS applications can also improve the resource efficiency of TTS models, especially neural network-based models, which have high computational complexity and memory requirements. Resource efficiency can reduce the cost and energy consumption of TTS models, and make them more accessible and scalable for various devices and platforms.
- Quality improvement: Reducing inference time for TTS applications can also enhance the quality, naturalness, and diversity of synthesized speech, by minimizing the delays, packet losses, or jitter caused by network congestion or other factors. Quality improvement can increase the satisfaction and trust of users and voice talent, and prevent or mitigate the harm or misuse of synthetic voices.
Emotion and Expressiveness
Some of the ongoing research in adding emotion and expressiveness to TTS voices are:
- Emotion intensity input: This research involves using an emotion intensity input from unsupervised extraction to improve emotional TTS. The emotion intensity input is derived from an attention or saliency map of an emotion recognizer, which indicates the regions of speech that are more emotional. The emotion intensity input can be used to control the degree of emotion expression in the synthetic speech.
- Emotion and style embeddings: This research involves using unsupervised methods to extract emotion and style embeddings from reference audio on a global, clustered, or frame level. The emotion and style embeddings can capture the variations of prosody and voice quality in different emotions and styles. The emotion and style embeddings can be used to condition the TTS model to generate speech with the desired emotion and style.
- Emotion conversion: This research involves using techniques such as voice or emotion conversion to generate emotional speech from neutral speech. Emotion conversion can modify the prosodic and spectral features of speech to change the perceived emotion of the speaker. Emotion conversion can be used to augment the emotional data for training TTS models, or to synthesize speech with different emotions from the same text input.
Considering the above the next important factor is the significance of emotional speech synthesis in various domains:
- Virtual assistants: Emotional speech synthesis can enhance the naturalness and interactivity of virtual assistants, such as Siri, Alexa, or Cortana, by enabling them to express different emotions and styles according to the context and user feedback. Emotional speech synthesis can also improve the user satisfaction and trust in virtual assistive technology, by making them more engaging and empathetic.
- Entertainment: Emotional speech synthesis can enrich the entertainment industry, such as video games, movies, or audiobooks, by creating realistic and diverse synthetic voices for characters, narrators, or singers. Emotional speech synthesis can also enable new applications and scenarios for entertainment, such as voice cloning, voice conversion, voice impersonation, and voice watermarking.
- Accessibility: Emotional speech synthesis can improve the accessibility and inclusion of people with disabilities or special needs, such as visual impairment, dyslexia, or aphasia, by providing them with expressive and personalized synthetic speech for communication or information. Emotional speech synthesis can also support the emotional well-being and mental health of people with disabilities or special needs, by providing them with emotional feedback or companionship.
Integration with AI Assistants and IoT Devices
Integration of TTS technology with AI assistants and IoT devices.
Some of the developments in the integration of TTS technology with AI assistants and IoT devices are:
- Azure Neural TTS on devices: Azure Neural TTS is a powerful speech synthesis service that allows users to turn text into lifelike speech using AI. Azure Neural TTS has recently announced the availability of natural on-device voices for disconnected and hybrid scenarios, such as screen readers, voice assistants in cars, or embedded devices. Azure Neural TTS on devices can provide high quality, high efficiency, and high responsiveness for speech synthesis on various devices and platforms.
- Google Cloud Text-to-Speech API: Google Cloud Text-to-Speech API is a cloud-based service that enables users to synthesize natural-sounding speech with Google’s groundbreaking neural networks. Google Cloud Text-to-Speech API supports more than 140 languages and variants, and allows users to customize the pitch, speaking rate, and voice profile of the synthetic speech. Google Cloud Text-to-Speech API also supports custom voice creation and voice tuning for creating unique and personalized voices for different brands and applications.
UberTTS is an advanced text-to-speech program combining the capabilities of both the above-mentioned Azure & Google AI technologies into one along with the usage of full SSML features.
- Speech On-Device: Speech On-Device is a solution that enables users to run server-quality speech AI locally on any device, such as phones, tablets, cars, TVs, or speakers. Speech On-Device can provide fast and reliable speech recognition and synthesis without network connectivity or latency issues. Speech On-Device can also support multilingual and cross-lingual speech capabilities for diverse user scenarios and preferences.
It is also important to discuss the benefits of incorporating TTS in smart home systems, healthcare, and accessibility solutions are:
- Smart home systems: TTS can enhance the functionality and interactivity of smart home systems, such as smart speakers, smart displays, or smart appliances, by enabling them to communicate with users using natural and expressive speech. TTS can also improve the user experience and satisfaction of smart home systems, by making them more engaging and personalized.
- Healthcare: TTS can improve the quality and accessibility of healthcare services, such as telemedicine, health education, or mental health support, by providing users with lifelike and customized speech synthesis. TTS can also reduce the cost and time of healthcare delivery, by enabling remote and efficient communication between patients and providers.
- Accessibility solutions: TTS can empower people with disabilities or special needs, such as visual impairment, dyslexia, or aphasia, by providing them with speech output for communication or information. TTS can also support the emotional well-being and inclusion of people with disabilities or special needs, by providing them with emotional feedback or companionship.
Frequently Asked Questions (FAQs)
Which is the first text-to-speech software?
The first text-to-speech software was Kurzweil Applied Intelligence, which released the first commercially available speech recognition software for personal computers in 1984. However, the first speech synthesis systems were computer-based and developed in the late 1950s by Bell Laboratories and IBM. The first mechanical speech synthesizer was developed by Charles Wheatstone in the early 1800s.
Who started TTS?
There is no definitive answer to who started TTS, as different researchers and companies contributed to the development of speech synthesis and recognition systems over the years. However, some of the pioneers of TTS include:
- Christian Kratzenstein, a German-Danish scientist who created acoustic resonators that mimicked the sound of the human voice in the 1700s.
- Charles Wheatstone, a British inventor who developed the first mechanical speech synthesizer in the early 1800s.
- Homer Dudley, an American electrical engineer who created the VODER (Voice Operating Demonstrator), the first electronic speech synthesizer, in 1939.
- John Larry Kelly Jr., a physicist at Bell Labs who used an IBM computer to synthesize speech in 1961.
- Noriko Umeda et al., researchers at the Electrotechnical Laboratory in Japan who developed the first general English text-to-speech system in 1968.
- Ray Kurzweil, an American inventor who released the first commercially available speech recognition software for personal computers in 1984.
What is the history of synthetic speech?
The history of synthetic speech can be summarized as follows:
- The history of synthetic speech dates back to the 1700s, when some researchers and inventors tried to build mechanical devices that could produce human-like sounds, such as acoustic resonators and speech synthesizers.
- The history of synthetic speech advanced in the 20th century, when electronic and computer-based systems were developed to generate speech from text or other inputs, such as the VODER, the IBM computer, and the Electrotechnical Laboratory system.
- The history of synthetic speech progressed further in the late 20th and early 21st century, when new techniques and technologies were introduced to improve the quality, naturalness, and diversity of synthetic speech, such as neural networks, voice cloning, and emotion and style embeddings
What is the history of speech recognition in AI?
The history of speech recognition in AI can be summarized as follows:
- Speech recognition is the technology that enables computers to recognize and translate spoken language into text.
The first speech recognition system was developed by Bell Laboratories in 1952 and could recognize spoken numbers with high accuracy. - In the 1960s and 1970s, speech recognition systems expanded their vocabulary and used probabilistic methods such as Hidden Markov Models to improve accuracy and speed.
- In the 1980s and 1990s, speech recognition systems became more speaker-independent and used neural networks and statistical language models to handle natural language and large vocabularies.
- In the 2000s and 2010s, speech recognition systems benefited from advances in deep learning and big data, achieving near-human performance in various domains and applications.
What is speech synthesis technology?
Speech synthesis technology refers to the process of generating artificial speech from digital text input. This technology is commonly used in devices and software that require an audio output of written content.
When were speech synthesis systems created?
The first speech synthesis systems were created in the 1770s by Wolfgang von Kempelen and Russian Professor Christian Kratzenstein. These acoustic-mechanical speech machines were the first devices to be considered as speech synthesizers.
What was the first device to be considered a speech synthesizer?
The first device to be considered a speech synthesizer was the Voder, which was created by Homer Dudley in the late 1930s. It was able to produce a limited range of human-like sounds and was used primarily for early voice coding experiments.
How has synthesis technology evolved over time?
Synthesis technology has evolved considerably since the creation of the Voder. In the 1970s, Texas Instruments produced the first complete text-to-speech system, known as the “Speak & Spell.” The development of unit selection synthesis in the 1980s allowed for more natural sounding speech by piecing together pre-recorded words and phrases. The introduction of spectrogram techniques and linear predictive coding in the 1990s further improved the quality of synthesized speech. Currently, natural language processing algorithms are used to generate highly realistic and intelligible speech.
What is a vocoder?
A vocoder is a type of speech synthesizer that works by analyzing and synthesizing the characteristics of speech signals. It was originally invented for secure communication during World War II and has since been used in music production to create robotic vocals.
What is unit selection synthesis?
Unit selection synthesis is a technique where pre-recorded units of speech, such as words or phrases, are selected based on their phonetic and prosodic features and pieced together to create natural-sounding speech.
What is intelligible speech?
Intelligible speech refers to speech that can be understood by a listener. In the context of speech synthesis, it refers to the ability of synthesized speech to be perceived as clearly and accurately as natural speech.
What is Dectalk?
Dectalk is a speech synthesizer that uses concatenative synthesis, which is another form of unit selection synthesis. It was commonly used in assistive technology devices for the visually impaired or those with reading difficulties.
What is Haskins Laboratories?
Haskins Laboratories is a private, non-profit research institute focused on the study of speech, language, and cognitive processes. They have conducted extensive research on speech synthesis technology.
How is text turned into audio?
Text is turned into audio through the process of speech synthesis. This process involves breaking down the text into phonetic and linguistic elements and using synthesis technology to generate speech signals that are then converted into an audio output.
Final Thoughts
Based on all that we discussed above a possible conclusion of the evolution of TTS technology from robotic voices to natural human-like speech could be that:
TTS technology has undergone significant advances in the past decades, from producing robotic and monotonous voices to generating lifelike and expressive speech. The main drivers of this evolution are the development of new synthesis techniques, such as neural network-based models, the availability of large and diverse speech data, and the application of transfer learning and data augmentation methods.
The evolution of TTS technology has enabled new capabilities and features, such as voice cloning, emotion and style embeddings, and voice tuning. The evolution of TTS technology has also enabled new applications and scenarios, such as voice assistants, entertainment, and accessibility solutions.
The evolution of TTS technology has also brought new challenges and opportunities, such as ethical concerns, quality evaluation, and user experience. The evolution of TTS technology is expected to continue in the future, as more research and innovation are conducted in this field.
