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Giving software a natural voice for calls, videos and assistants by reading text aloud.
Neural TTS generates speech with a trained model, so pauses and intonation sound natural. You choose a voice, a language and sometimes a style.
Text-to-speech (TTS) converts written text into audio of a person speaking it. Older systems stitched together recorded fragments and sounded robotic. Neural TTS generates the waveform with a trained model, so pauses, stress and intonation sound much closer to a real speaker.
You usually choose a voice (a stock voice, or a custom one trained or cloned from recordings), a language, and sometimes a style such as calm or cheerful. Many engines accept SSML, a markup standard for controlling pronunciation, pauses and emphasis.
Numbers, dates, currency and abbreviations are expanded into words.
Numbers, dates, currency and abbreviations are expanded into words: "₹1,250" becomes "one thousand two hundred and fifty rupees".
Text becomes speech sounds with predicted stress and rhythm.
The text is converted to phonemes (speech sounds) with predicted stress and rhythm.
A neural network predicts the sound of those words in the chosen voice.
A neural network predicts a spectrogram or audio codes for those sounds in the chosen voice.
A second model turns that into the final audio you hear.
A second model turns that representation into the final audio waveform. Some newer models do the last two steps in one.
An AI agent answers the phone and TTS speaks its replies.
An AI agent answers the phone, and TTS speaks its replies. Speed of the first audio chunk decides whether the conversation feels natural.
Narration for explainers and courses in several languages, without a studio for every edit.
Narration for product explainers and course videos in several languages, without booking a studio for every edit.
Reading out pages, documents and app screens for people who prefer to listen.
Reading out web pages, documents and app screens for people who prefer to listen.
A clinic sends Tamil and English appointment reminders by voice, normalising numbers and times first because misread times draw the most complaints.
A clinic wants appointment reminders in Tamil and English. A scheduled automation pulls tomorrow's appointments, builds a short script for each patient (name, time, doctor, how to reschedule), sends it to a TTS engine, and places the call or sends the audio as a voice note. Numbers and times go through a normalisation step first, because "10:30" read wrongly is the most common complaint in this kind of system.
Use a stock voice or get the speaker's documented permission before cloning.
Use a stock voice or get documented permission from the speaker. Many TTS vendors require this in their terms.
In live calls, stream audio sentence by sentence instead of waiting for the full reply.
In live calls, generate and stream audio sentence by sentence instead of waiting for the full reply.
Brand, doctor and place names need a custom lexicon or SSML hints.
Brand names, doctor names and local place names need a custom lexicon or SSML hints.
Yes. Open models such as Piper run on modest hardware, including a Raspberry Pi. Hosted APIs usually offer more voices and languages, so the choice is privacy and cost against voice quality.
Only with that person's clear permission, and you should keep a record of it. A cloned voice is personal data and can also raise impersonation and fraud issues, so treat it with the same care as a photograph or signature.
Speech Synthesis Markup Language is a W3C standard for adding instructions to TTS input: pauses, emphasis, how to say a number, or a phonetic spelling for an unusual name.
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