How Text to Speech Works, From Characters to Natural Voice

How Text to Speech Works, From Characters to Natural Voice

Text to speech turns written text into spoken audio. Behind the scenes, it reads the characters you give it, figures out how they should sound, predicts timing and melody, then renders a waveform you can play on any device.

The basics

If you want the short version, here it is.

• You provide text, optionally with SSML markup.

• The system cleans the text, chooses pronunciations, and sets timing and melody.

• A neural model predicts a spectrogram, then a vocoder turns it into audio you can play.

• You can steer rate, pitch, pauses, and names using SSML or a small pronunciation list.

• Natural sound relies on clear pronunciation, good emphasis, fluid blending of sounds, and a steady rhythm.

• Fast response comes from streaming and right sized audio settings.

What text to speech means

Text to speech, or TTS, is software that reads text aloud using a synthetic voice. It is different from voice cloning. TTS can read any text with a preset voice, while cloning tries to copy a specific person. You pick a language, a voice, and sometimes a style like conversational or news. The system then speaks the text with the best pronunciation it can produce.

TTS shows up in the tools we use every day. Screen readers make apps usable without looking at the screen. Navigation apps speak directions. Customer support systems handle long queues. Content creators generate narration for short videos or tutorials. Anywhere you see text and want sound, TTS is useful.

The pipeline from text to audio

Most modern systems follow a similar pipeline. The names of the blocks change across vendors, but the steps line up like this.

1. Text normalization. Expand numbers, dates, and abbreviations into how people say them. For example, “3rd Jan” becomes “third of January.”

2. Tokenization and linguistic features. Split text into words and symbols, mark sentence boundaries, and capture hints like punctuation and capitalization that affect speaking.

3. Pronunciation with phonemes. Map words to sound units. This can use a dictionary, a grapheme to phoneme model, or both. Good systems handle names, acronyms, and borrowed words with fallback rules.

4. Prosody and duration. Predict where to pause, which words to stress, and how long each phoneme should last. This step shapes rhythm and melody so the result sounds like a person, not a metronome.

5. Acoustic model to spectrogram. A neural network turns the phonemes and prosody into an acoustic picture called a mel spectrogram. You can think of it as a heat map of frequencies over time.

6. Vocoder to waveform. A second model converts the spectrogram into audio samples. Classic examples include WaveNet, WaveRNN, and HiFi-GAN. The output is a playable waveform in formats like WAV, MP3, or OGG.

Modern stacks vary. Some use two models, one for acoustics and one for the vocoder. Others are end to end. Some predict audio frame by frame, others generate in parallel for speed. The high level flow stays the same.

What makes a voice sound natural

Natural speech is a mix of clean pronunciation, consistent tone, and well timed pauses. Small details add up.

• Pronunciation accuracy. Words sound right, including names and rare terms. Numbers, units, and dates expand correctly.

• Prosody. Emphasis lands on the right words. Questions rise at the end. Lists sound like lists, not a run on sentence.

• Coarticulation. Sounds blend in a way that feels human. The transition between words is not choppy.

• Timbre and noise. The voice has a clear character without hiss or buzz. Breaths and mouth sounds are controlled.

• Long form stability. Over long paragraphs the voice stays consistent, avoids pitch drift, and keeps a steady rhythm.

Controls that matter, SSML and pronunciation

You get better results when you guide the model. Two tools matter most.

1. SSML. This is an XML format that lets you set speaking rate, pitch, volume, pauses, and how to say specific things. Support varies by provider, and you can control things like:

• Speaking rate, faster or slower.

• Pitch and volume.

• Pauses and breaks.

• Reading style for numbers, dates, or individual letters.

• Custom pronunciations for names and product terms.

1. Pronunciation dictionaries. You can supply word to phoneme entries for product names or local places. Many systems accept IPA. Some accept other phoneme sets. Keep these lists in source control and review them as your content grows.

Practical tips. Split very long text at natural punctuation. Add short pauses around list items. Mark up acronyms and units. Test tricky names early so you do not fix them at the last minute.

Quality and latency, how they are measured

Teams check quality with listening tests. The common one is MOS, where people rate samples on a five point scale. Preference or A B tests compare two versions and ask which one sounds better. For automatic checks, engineers use measures like mel cepstral distortion to catch regressions, but human ears still decide if it works.

Latency comes from three places, text processing, the acoustic model, and the vocoder. Real time apps aim for an overall real time factor below one, which means the system can speak at least as fast as it plays. Streaming helps, the server sends audio in chunks as it renders, so playback can start before the whole sentence is ready.

You can keep latency low with a few choices. Use streaming endpoints when available. Limit sample rate to what you need. For most voices, 22 or 24 kHz sounds good and costs less time than 48 kHz. Cache common phrases like greetings. Keep requests short, then queue the next sentence while audio is playing.

That is the core of TTS. Feed it text, help it pronounce things, guide the rhythm and timing, and you will get clear, natural speech that fits your app.