Text-to-Speech (TTS)

Text-to-speech (TTS) is an AI technology that synthesises human-sounding speech from written text. Modern TTS systems use deep learning models to produce audio that closely resembles natural human speech, including appropriate intonation, rhythm, emphasis, and emotional expression. Early TTS systems relied on concatenating pre-recorded speech fragments, producing robotic-sounding output. Current neural TTS models generate audio waveforms directly from text, producing far more natural results. Some systems can clone specific voices from short audio samples, enabling personalised voice experiences. The TTS pipeline typically involves two stages: a text-to-spectrogram model that converts text into a mel spectrogram (a visual representation of the audio frequencies), and a vocoder that converts the spectrogram into an actual audio waveform. Modern end-to-end systems combine these stages for improved quality and lower latency.

TTS enables businesses to create voice-based interactions and audio content at scale. Voice assistants, IVR (interactive voice response) systems, audiobook generation, podcast creation, and in-app narration all rely on TTS technology. Accessibility is a major driver of TTS adoption. Organisations use TTS to make written content accessible to visually impaired users, support users who prefer audio consumption, and provide multilingual voice output for global audiences. Many accessibility regulations require or encourage voice alternatives to text content. The quality of modern TTS has reached a level where it is increasingly difficult to distinguish synthesised speech from human recordings. This opens opportunities for creating audio content — training materials, product announcements, customer communications — at a fraction of the cost and time of traditional voice recording.