What is an example of text to speech?
Text-to-speech (TTS) is a type of assistive technology that converts digital text into synthesized speech. The first TTS systems emerged in the 1970s and 1980s as a way to provide speech output for various applications like screen readers for the visually impaired (https://www.readingrockets.org/topics/assistive-technology/articles/text-speech-technology-what-it-and-how-it-works, viewed on date). TTS technology works by having a computer or device read text aloud using an artificial synthesized voice.
The process of generating speech from text involves two main components (https://www.cs.cmu.edu/~srallaba/Learn_Synthesis/intro.html, viewed on date):
- Natural language processing – Analyzing and preprocessing the input text
- Digital signal processing – Converting the preprocessed text into synthesized human-like speech
Some of the main use cases and applications for TTS include (https://huggingface.co/tasks/text-to-speech, viewed on date):
- Screen readers for visually impaired users
- Voice assistants like Siri, Alexa, Google Assistant
- Navigation systems in cars/transport
- Audiobooks and ebook readers
- Voice interaction systems
Text-to-Speech vs Speech Synthesis
Text-to-speech (TTS) and speech synthesis are closely related technologies often used interchangeably. However, there are some key differences between them:
Speech synthesis is the broader technology that generates artificial speech from text (https://en.wikipedia.org/wiki/Speech_synthesis). It encompasses TTS, which specifically converts written text into speech. Speech synthesis systems can produce speech not just from text, but also from other inputs like phonetic transcriptions.
TTS is focused solely on taking normal language text and converting it into natural sounding speech. It relies heavily on natural language processing to analyze and preprocess the input text before feeding it into a speech synthesizer to generate audible speech. The goal of TTS is to sound as human-like as possible.
Some key strengths of TTS systems are their ability to output synthesized speech quickly from any input text, support multiple languages, and allow customization of speech output. Speech synthesis offers more flexibility by supporting non-text inputs, but quality can suffer compared to dedicated TTS engines. Overall, TTS represents the most advanced form of speech synthesis aimed at mimicking human speech.
Text-to-Speech Engines and APIs
There are several major text-to-speech engines and APIs available today that convert text into synthesized speech. Some of the leading options include:
AWS Polly – Amazon Web Services offers the Polly text-to-speech engine and API. According to TechRadar, Polly provides very natural sounding voices and support for 47 different languages and accents. It is highly customizable in terms of speech rate, pitch, volume and more.
Google WaveNet – Developed by DeepMind, Google’s WaveNet uses deep neural networks to generate speech waveforms. As noted by G2, it produces some of the most human-like synthesized voices available today, but can be computationally expensive.
Microsoft Azure – Microsoft’s text-to-speech service runs on neural networks and offers natural-sounding voices. It supports over 75 voices and languages. The Azure API provides customizable speech output and SSML support.
Some other leading text-to-speech engines include IBM Watson, Nuance Vocalizer, Lyrebird, and more. The major APIs provide client libraries and SDKs for easy integration into applications across languages like Python, Java, JavaScript, C# and others.
When selecting a text-to-speech engine, it’s important to consider factors like naturalness of speech, language and voice support, customizability, pricing, and ease of implementation. While services like Google WaveNet may produce the most human-sounding results, the high computational cost may not make sense for some use cases. The ideal solution depends on the specific application requirements.
Natural Language Processing for Text-to-Speech
Natural language processing (NLP) plays a key role in modern text-to-speech systems. NLP techniques allow text-to-speech engines to analyze the context and meaning behind text in order to generate more natural sounding speech with proper pronunciation and prosody. According to Text-to-Speech and Natural Language Processing, the latest breakthroughs in NLP have significantly improved text-to-speech capabilities.
Specifically, NLP enables text-to-speech systems to go beyond simply reading text word-for-word. With NLP, the system can determine the linguistic context, grammar, punctuation, and semantics of the input text. This allows the engine to apply the proper intonation, pauses, emphasis, pitch, and emotion when synthesizing speech based on the meaning. For example, NLP techniques can recognize differences between a statement, question, or exclamation to modulate the speech accordingly. NLP can also identify entities, relationships, parts-of-speech, and other syntactic and semantic attributes to improve pronunciation, inflection, and fluidity.
However, NLP for text-to-speech still faces challenges. As noted in Text to Speech using Natural Language Processing, completely understanding the contextual meaning and intent behind text input requires highly advanced AI. State-of-the-art NLP algorithms can struggle with nuances in linguistic expression, sarcasm, wit, metaphors, and other complex human language constructs. There is still progress to be made in contextual NLP to reach human-level comprehension for text-to-speech applications.
Text Preprocessing for Text-to-Speech
Text preprocessing is an important step in producing high-quality and natural sounding speech from text. It involves normalizing the input text by converting numbers, abbreviations, dates, units, etc. into their spoken form before feeding them into the TTS system (Reichel, 2006). Proper text preprocessing significantly improves the accuracy and fluency of the synthesized speech.
Text preprocessing typically involves tokenization, normalization, expansion of abbreviations/acronyms, conversion of numbers and dates into words, and filtering disfluencies. Tokenization splits the text into individual words, phrases, symbols, etc. Normalization converts text into a standard format by transforming abbreviations, case formats, punctuation, etc. (e.g. converting “I.B.M.” to “IBM”). Expanding abbreviations and acronyms is crucial for proper pronunciation (e.g. “NASA” -> “National Aeronautics and Space Administration”). Numbers and dates must also be converted into their word equivalents (e.g. “10/12/2022” -> “October twelfth, two thousand twenty two”) to be read correctly.
Special care must be taken when preprocessing abbreviations and numbers, as they can be highly ambiguous without surrounding context. For example, determining whether “Dr.” refers to “Doctor” or “Drive” depends on the context. And a number like “100” could mean “one hundred” or “a hundred” depending on whether it refers to an exact quantity or an estimate. Advanced NLP techniques are often required to handle such cases appropriately.
Overall, high-quality text preprocessing that accounts for the nuances of human language is essential for producing natural and intelligible speech from text via TTS systems.
Challenges and Limitations
While text-to-speech technology has improved drastically in recent years, there are still some key challenges and limitations to overcome. One major challenge is accurately conveying accent, tone, emotion, and pronunciation. Most text-to-speech systems speak in a default voice that lacks expressiveness and sounds robotic. As noted by PrimeVoices, “The quality of TTS audio is generally lower than professional voice recordings” (https://primevoices.com/blog/pros-cons-of-tts-audio/).
Text-to-speech engines also struggle to capture the full context and nuance of language. Subtleties like sarcasm, wordplay, metaphors, and cultural references are often missed. As Speechify points out, text-to-speech voices “can sometimes mispronounce words, speak in a monotone, and fail to provide the nuance and inflection of human speech” (https://speechify.com/blog/are-text-to-speech-voices-good/). This makes text-to-speech less suitable for complex or narrative content.
There are still limitations around accurately translating abbreviations, acronyms, names, places, and other ambiguities. As noted by Murf.ai, common issues include “Inaccurate pronunciation” and “Lack of emotion or expression” (https://murf.ai/resources/text-to-speech-voice-generation-common-issues-and-solutions/). Overall, there is ample room for improvement in making text-to-speech voices sound more natural, fluent, and human-like.
Current Applications
Text-to-speech has become an integral technology in various real-world applications today. Some of the major current uses of text-to-speech include:
Use in accessibility tools and screen readers – Text-to-speech allows the conversion of text into speech for visually impaired individuals to access digital content through screen readers. Popular screen readers like JAWS and VoiceOver integrate text-to-speech engines to read out texts from websites, books, documents etc. [1]
Text-to-speech in virtual assistants, GPS etc. – Intelligent personal assistants like Siri, Alexa and Google Assistant use text-to-speech to provide voice responses and notifications to users. Text-to-speech also enables turn-by-turn voice navigation in GPS systems.
Applications in learning and content creation – Text-to-speech can aid in developing contents like e-books, online courses, podcasts etc. It is also useful for children in learning to read or assisting people with learning disabilities.
Future Directions
Advances in deep learning and neural networks are rapidly improving text-to-speech systems. End-to-end neural text-to-speech (NTTS) models like Tacotron 2 and FastSpeech 2 are producing more natural-sounding and human-like voices by better modeling the complex relationship between text and speech (https://fliki.ai/blog/future-text-to-speech). These models can generate high-quality spectrograms from text in a single step, streamlining the traditional text-to-speech pipeline. NTTS systems are also highly customizable, enabling the creation of personalized voices.
Another major trend is the development of multi-speaker and personalized text-to-speech models. Using techniques like transfer learning and meta-learning, text-to-speech systems can now synthesize natural-sounding speech in a variety of speaker identities and accents with just a small speech sample (https://www.forbes.com/sites/sunilrajaraman/2024/01/21/how-to-use-artificial-intelligence-today-text-to-speech-technology/). This allows for highly personalized and customizable voices tailored to individual users or contexts.
Overall, deep learning advancements are making synthesized speech sound increasingly human-like. Text-to-speech systems are becoming more personalized, customizable and applicable across a wider range of use cases.
Text-to-Speech vs Human Speech
There are several key differences between text-to-speech and human voiceovers. Text-to-speech technology uses advanced artificial intelligence to convert text into speech, while human voiceovers involve real people recording audio.
When it comes to perception and acceptance, research shows that human voices are generally preferred and viewed as more natural sounding. However, text-to-speech voices have improved significantly in recent years, approaching human-like quality in some cases (Are Text To Speech Voices Good).
Text-to-speech may be preferred in some situations where human voiceovers are impractical or uneconomical, such as for extremely long content. Text-to-speech also allows for instant voice generation and limitless scaling, while human voices require recording and production time (Human vs AI Audio: Quality, Cost, & Time Comparison).
Overall, while human voices are more natural sounding, text-to-speech offers advantages like scalability and quick turnaround. The choice depends on factors like perception, cost, production time, and length of content. As text-to-speech quality continues improving, its applications may expand further.
Example Text-to-Speech Systems
Here are some leading examples of text-to-speech systems and audio samples demonstrating their voice quality:
Google Text-to-Speech
Google’s text-to-speech engine is one of the most popular and widely-used. It provides natural-sounding voices in multiple languages and accents. Listen to an audio sample:
Google’s WaveNet voices aim to mimic human speech patterns and inflections. The results sound very smooth and natural.
Amazon Polly
Amazon Polly is another leading text-to-speech service, providing high-quality voices. Here’s an example:
Polly creates very natural sounding speech from text input. The voices are clear and intelligible.
Microsoft Azure Text-to-Speech
Microsoft also provides a robust text-to-speech service as part of Azure. Listen to a sample:
The Azure text-to-speech voices are very natural and human-like. The audio sounds smooth and expressive.
As you can hear from these examples, modern text-to-speech services can synthesize speech that is highly intelligible and natural-sounding. The voice quality continues to improve over time as well.