Text to speech (TTS) is a technology that converts written text into speech. Thanks to advanced algorithms and voice models, TTS enables the generation of natural-sounding speech from any textual content. The technology is widely used across industries, from customer service, through voice assistants, to support for people with disabilities, enabling automation of communication processes and access to information.
Text to Speech (TTS)
Type of technology
Description of the technology
Basic elements
- Text conversion module: It is responsible for analysing and understanding textual content.
- Speech synthesiser: It generates speech based on the transformed text, adapting it to natural language patterns.
- Library of voices: It includes voice data sets used to create realistic speech sounds.
- The function of intonation and rhythm: It makes it possible to adapt speech to natural fluctuations, such as pauses, accents, and intonation.
- AI algorithms: They support speech modelling based on natural language and learn to correctly interpret different speech styles.
Industry usage
- Voice assistants: Siri, Alexa, Google Assistant use TTS to interact with users.
- Call centre: Automated voice response systems for customer service.
- Education: Transforming educational content into speech to support learning by listening.
- Health care: Systems that support the blind and visually impaired to enable them to use textual content.
- Marketing: Generating voice ads and promotional content.
Importance for the economy
TTS technology is widely used in sectors such as customer service, marketing, health care, education, and entertainment. Automating voice interactions in call centre or chatbots significantly reduces companies’ operating costs while improving the quality of service. With TTS, it is possible to create more accessible content for the blind and visually impaired and improve communication on smart devices. In the future, TTS will be a key element in the development of voice interfaces.
Related technologies
Artificial Intelligence
Integration with operating systems and mobile applications to automate voice interfaces.
Cloud Computing
AI makes it possible to adapt speech to context and improve the naturalness of voices.
Mechanism of action
- TTS systems first analyse the input text, segmenting it into linguistic units, such as words, phrases, and sentences. Then, the speech synthesiser transforms units into sounds that correspond to the phonemes based on a library of voices. Intonation algorithms adjust tone, accents, and pauses to make the generated speech sound natural and easy to understand. More advanced systems use machine learning models that refine the way speech is generated by analysing language patterns in context.
Advantages
- Availability: Facilitating access to text content for people who are blind or have difficulty reading.
- Communication automation: It enables automatic generation of voice messages in customer service systems.
- Naturalness of speech: Modern algorithms generate speech that sounds very natural.
- Personalisation: A choice of different voices, accents, and languages to suit user needs.
- Broad application: Use in smart devices, entertainment systems, education, and marketing.
Disadvantages
- Improper use: TTS can be used to generate false messages, increasing the risk of disinformation.
- Lack of emotional depth: Despite greater naturalness, generated speech may not convey the full range of emotions.
- Interpretation problems: TTS may have difficulty correctly processing ambiguous texts or specialised texts.
- Language barrier: Problems with generating speech in less common languages or dialects.
- High implementation costs: Implementing high-quality TTS systems can be expensive.
Implementation of the technology
Required resources
- Software: TTS tools to support real-time speech synthesis.
- Voice databases: Collections of voices and phonemes for training synthesis models.
- Computing power: Powerful infrastructure for text processing and speech generation.
- Development team: Experts responsible for the development and optimisation of TTS systems.
- Access to language data: Textual data needed to train language and voice models.
Required competences
- Machine learning: Knowledge of AI models used in speech synthesisers.
- Natural language processing (NLP): Ability to process and interpret textual data.
- Sound engineering: Knowledge of sound generation and speech modulation.
- Programming: Ability to work with TTS technologies in environments such as Python and TensorFlow.
- IT project management: Coordination of activities related to the implementation of TTS in various applications.
Environmental aspects
- Energy consumption: Real-time speech generation in large systems requires considerable energy resources.
- Recycling: Replacing and updating equipment that supports TTS systems generate electronic waste.
- Emissions of pollutants: The development of data centres that support advanced TTS systems can contribute to CO2 emissions.
- Raw material consumption: Manufacturing the equipment needed to process speech data requires raw materials, such as rare earth metals.
Legal conditions
- Legislation governing the implementation of solutions, such as AI Act (example: regulations on accountability for the use of AI in communications).
- Safety standards: Regulations for securing TTS-generated content (example: ISO/IEC 27001 information security standards).
- Intellectual property: Protection of copyright related to TTS-generated voices (example: copyright on synthetic voices).
- Data security: Regulations for the protection of personal data in TTS systems (example: GDPR in the EU).
- Export regulations: Regulations for the export of advanced speech processing technology (example: restrictions on the export of TTS technology to sanctioned countries).