How Text-to-Speech (TTS) Works, Step by Step (2026)

How Text-to-Speech Works

A voice is a sound wave stored by computers as a stream of numbers. Text-to-speech (TTS) technology converts written text into these numerical streams to produce human-like speech.

The Classic Four-Step Recipe

1. Text Normalization: This step cleans up the text by converting written shortcuts, symbols, and abbreviations into fully written words (such as converting "$200" to "two hundred dollars"). Context is used to resolve ambiguities like "Dr." (Doctor or Drive). Common problem areas include phone numbers, prices, dates, and email addresses. Solutions include pre-formatting the text or using Speech Synthesis Markup Language (SSML).
2. Letters to Sounds (Phonemes): Written letters are translated into phonemes, which are the distinct sounds of a language. Because spelling is often inconsistent (as in "though," "through," and "tough"), systems rely on pronunciation dictionaries, rules, or small models to determine the correct sounds. Some modern models skip this step and learn directly from letters.
3. Prosody (Rhythm and Melody): This step plans the rhythm, pitch, pauses, and emphasis of the speech. Since a single sentence can be spoken in many different ways (the "one-to-many" problem), models predict the duration, pitch, and volume of each sound. Punctuation plays a critical role here, with commas indicating pauses and question marks altering the melody.
4. Making the Sound Wave: Traditional systems generate the final audio in two parts: an acoustic model creates a spectrogram (a visual representation of the sound), and a vocoder converts that spectrogram into an actual sound wave. Newer end-to-end models can generate the sound wave directly from text or phonemes in a single network.

Alternative Modern Approaches

• Speech as Tokens: Some models treat speech like a language. A neural codec compresses audio into discrete tokens (codes representing sounds), and a language model predicts the next token in the sequence, similar to how text-based chatbots predict the next word. While highly expressive and capable of voice cloning from short samples, these models can occasionally hallucinate, repeat, or skip words.
• Noise-Refining: Another family of models generates speech by progressively refining random noise into structured audio, similar to how AI image generators work. Many open-source models mix language models, codecs, and noise-refining techniques.

Streaming and Latency

For interactive voice agents, minimizing the "time to first audio" is crucial. To achieve this, TTS systems stream audio, playing the first words of a response while the rest of the sentence is still being generated. While processing text in smaller pieces reduces latency, it can result in a loss of context and unnatural melody jumps at the seams. Advanced systems use techniques like continuations to maintain context across streamed segments.

Testing TTS Systems

Before selecting a TTS model for a voice agent, it is recommended to conduct several tests:

• Test the model using your own text, specifically including your product names, numbers, dates, and addresses.
• Measure the "time to first audio" for both typical and slow responses.
• Listen to long replies to ensure the voice remains consistent throughout.
• Inquire about how the model was trained, including the source data and whose voices were used.