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How does text-to-speech work?

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Quick answer

Text-to-speech works by breaking text into phonemes, predicting how those sounds should be spoken with natural pitch and rhythm, and then generating an audio waveform. Early systems pieced together recorded fragments; modern neural systems learn the whole mapping from text to sound using large speech datasets.

Text-to-speech converts written text into spoken audio by analysing the words, predicting how they should sound, and synthesising a waveform. Modern neural systems learn these mappings from thousands of hours of recorded speech, producing natural-sounding voices.

Step by step

  1. The text is normalised: numbers, dates and abbreviations are rewritten as the words a speaker would say.
  2. The normalised text is converted into a sequence of phonemes, the basic sound units of the language.
  3. A model adds prosody—pitch, pauses and stress—so the sentence sounds like natural speech.
  4. An acoustic model converts the phoneme and prosody sequence into a spectrogram of sound frequencies.
  5. A vocoder turns the spectrogram into an audio waveform that a speaker or headphone can play.

Key things to understand

  1. The system first normalises the text, expanding abbreviations, numbers and symbols into spoken words.
  2. It then converts the words into phonemes, the distinct sound units of a language.
  3. A model predicts prosody—pitch, timing and stress—so the speech sounds natural rather than robotic.
  4. An acoustic model turns the phoneme and prosody sequence into a spectrogram, a picture of sound frequencies over time.
  5. A vocoder converts that spectrogram into an audio waveform that speakers can play.

Labelled diagram

  1. Text input

    Raw characters enter the system, including numbers and punctuation.

  2. Normalisation

    Abbreviations and symbols are expanded into spoken words.

  3. Phonemes

    Words are converted into the sound units of the language.

  4. Prosody model

    Pitch, timing and stress are added for natural rhythm.

  5. Acoustic model

    A spectrogram of sound frequencies is generated.

  6. Vocoder

    The spectrogram becomes a playable audio waveform.

From text to spoken audio

Real-world examples

  • Screen readers on smartphones use text-to-speech to read messages and web pages aloud for blind users.
  • Voice assistants like Siri and Alexa speak answers using text-to-speech after processing a spoken query.
  • Navigation apps such as Google Maps speak turn-by-turn directions, converting street names into audio in real time.
  • Audiobook platforms use text-to-speech to narrate books that have no human recording.

Common mistake

✗ Myth: Text-to-speech just reads letters aloud one by one.

✓ Fact: It works at the level of phonemes and words, not letters. The system uses context to decide pronunciation, so 'read' can sound like 'reed' or 'red' depending on the sentence.

People also ask

How does text to speech work step by step?
Text is first normalised so numbers and abbreviations become words. Then it is converted into phonemes, the sound units of the language. A model adds pitch and rhythm, an acoustic model makes a spectrogram, and a vocoder turns that into audio you can hear.
What is a vocoder in text to speech?
A vocoder is the final stage that converts a spectrogram—a visual representation of sound frequencies over time—into an actual audio waveform. Early vocoders sounded robotic; neural vocoders now produce speech that is hard to distinguish from a human recording.
Why does text to speech sound robotic sometimes?
Robotic speech comes from missing or unnatural prosody—flat pitch, wrong pauses and even stress. Older concatenative systems also produced clicks at the joins between recorded fragments. Modern neural systems reduce these artefacts by learning natural rhythm from real speech.
Why does text-to-speech sometimes mispronounce words?
Mispronunciations happen when the system guesses phonemes from spelling rather than meaning. Homographs like 'lead' or 'read' depend on context, and rare names or technical terms may not appear in training data, so the model falls back to a default pronunciation.
Can text-to-speech sound like a specific person?
Yes, if the system is trained on recordings of that person's voice. This is called voice cloning, and it raises ethical and legal concerns because it can be used to impersonate someone without consent.
What is the difference between concatenative and neural text-to-speech?
Concatenative systems stitch together small recorded fragments of human speech, which can sound choppy at joins. Neural systems generate the audio from a learned model, producing smoother and more expressive speech, though they need more computing power.
Does text-to-speech work for every language?
Not equally well. Languages with regular spelling-to-sound rules, like Spanish or Hindi, are easier to convert than English, whose pronunciation is irregular. For many low-resource languages, little recorded speech exists, so quality is lower.

Part of the guide: How AI Works →