Text-to-Speech
Text-to-speech converts written content into a spoken audio signal. The competence is controlling pronunciation, voice and delivery while preserving the intended words, then assessing intelligibility and playback behavior. Naturalness, timing and factual correctness are different qualities, so pleasing sound alone is insufficient evidence of a successful synthesis workflow.
What it is
A synthesis pipeline interprets text and produces acoustic structure, then generates a waveform. Some systems separate text normalization, pronunciation modeling, acoustic prediction and a vocoder; other models combine stages. Normalization decides how numbers, dates and abbreviations should be spoken. Prosody governs timing, stress and intonation, while voice characteristics determine aspects of the perceived speaker. Tacotron 2 illustrates predicting a mel spectrogram before waveform synthesis. The output depends on language and training coverage as well as text formatting. Speech synthesis is distinct from voice conversion, which changes properties of existing speech, and from recognition, which maps audio in the opposite direction.
What the work involves
Define the intended audience, language, voice and playback context. Prepare scripts with explicit treatment of names, units and ambiguous notation. Select synthesis controls supported by the chosen model, generate short trials and listen before producing full tracks. Check omitted or repeated words, pauses, loudness and encoding, and compare intelligibility across relevant listeners and devices. Preserve source text and generation configuration for revisions. The result should be a reviewed audio artifact whose spoken content matches the approved script and whose delivery meets the application's timing and accessibility requirements, with difficult pronunciations recorded for future reuse.
Illustrative example
An illustrative navigation guide must speak street names and distances. The developer creates a pronunciation test set containing local names, abbreviations and decimal values. They compare versions by listening and ask reviewers to transcribe the generated instructions. One reading turns a route number into an ordinary quantity, so the input script is normalized differently. Final checks cover the full instruction at the intended playback volume, including pauses that separate the turn direction from the distance.
Limits and common mistakes
A natural voice can mispronounce names, skip content or place emphasis in a misleading position. Quality varies by language and text domain, and unsupported controls may be ignored or behave unpredictably. Objective acoustic measures do not replace listening for intelligibility and fidelity. Synthesis reproduces source claims without verifying them. Voice identity and permitted usage need deliberate selection. Evaluate pronunciation, content completeness and delivery separately, and test the actual output encoding and playback chain before distribution.
Prerequisites
Related skills
- → is subcategory of: Audio AI
- ← is an instance of: ElevenLabs
Sources and further reading
- Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
Acoustic prediction and vocoder stages in Tacotron 2 speech synthesis.
- ElevenLabs: Text to Speech
Official example of model, voice and synthesis configuration in a production API.
Last updated: 2026-10-10