Text-to-speech

Early TTS stitched together recorded sound fragments or used rule-based synthesis, which sounded robotic. Modern neural TTS models learn from large amounts of recorded speech and generate audio that can be hard to tell apart from a human reading, with natural intonation and pauses. Some models are multilingual: the same voice can read text in several languages.

Most TTS systems can also return timing information, telling you when each word starts and ends in the audio. That is what makes word-by-word captions and animations synced to speech possible.

For short explainers, TTS removes the recording session: no microphone, no retakes, and a script change is a regeneration rather than a new recording. It also makes it practical to publish the same video in several languages.

There are points to check. Pronunciation of names, acronyms and numbers can be off, so listen before publishing. Voices come with license terms, and some free voices are not cleared for commercial use. Platforms and regulations increasingly ask you to disclose synthetic media, so label AI voices when required.

A script contains “SQL” and “2026”. In a first preview, the voice reads “SQL” as a word rather than the letters you wanted. Rewriting it as “S-Q-L” in the narration fixes it. Writing numbers the way they should be spoken, such as “twenty twenty-six”, is a common TTS habit for the same reason.