> For the complete documentation index, see [llms.txt](https://twbvoiceplaybook.clearglobal.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://twbvoiceplaybook.clearglobal.org/2.-what-is-voice-data/2.1-technologies-that-use-voice-data.md).

# 2.1 Technologies that use voice data

### Text-to-Speech (TTS)

[Text-to-Speech systems](#user-content-fn-1)[^1] convert written text into synthesized speech. These systems analyze text and create audio that copies human speech patterns. Modern TTS systems can:

* generate speech with a style of speaking and rhythm that sounds natural
* adapt to different speaking styles, accents, and moods
* read out text in real time to help people with accessibility needs
* allow people to use apps and services that are controlled by voice

To develop high-quality TTS systems, you generally need clean, studio-quality voice recordings. These systems work best when trained on data from controlled settings. There should be very little background noise, the audio level should be consistent, and the speakers should be professionals. To create natural-sounding synthesized speech, you need someone with good pronunciation who speaks clearly and expresses themselves in a fitting way. 

### Automatic Speech Recognition (ASR)

[Automatic Speech Recognition](#user-content-fn-2)[^2] systems, or speech-to-text, convert spoken language into written text. These systems:

* process audio recordings and identify elements of speech
* take spoken content and convert it into written text
* allow people to use voice assistants and interfaces that are controlled by voice
* allow automated transcription of meetings, interviews, and other spoken content

ASR systems work better when trained on voice data that is diverse and varied. It should include a wide range of speakers, accents, and settings. To be effective, ASR models[^3] need to be trained with data that reflects the full range of real-world speech. This should include different ages, genders, accents, speaking styles and acoustic settings (for example, in noisy traffic). 

[^1]: **Text-to-Speech (TTS):** Technology that converts written text into spoken language. It can also generate audio speech. You can find TTS in accessibility tools like screen readers and in virtual assistants. It brings written texts to life through spoken words. If your phone reads out the messages you get, or an audiobook tells you your favorite story, this is because of TTS technology.

[^2]: **Automatic Speech Recognition (ASR) or Speech-to-Text (STT):** ASR converts spoken language into text. It can be used in voice assistants and transcription services, for example. You may hear both terms, ASR and STT, and they are almost the same. But STT can be a semi-manual process, while ASR is fully automated. ASR is like a smart listener that turns spoken words into written text on your device. While it does create the text automatically, it often needs some human input to make sure everything is correct, it can make mistakes especially in languages that are less used in the digital space.

[^3]: **Model:** A computer-based system that has made use of data to learn patterns. It can make predictions or generate language. In speech technology, models use voice data to learn to recognize speech (converting audio to text) or to create speech (converting text to audio).