Text-to-Speech Testing

Text-to-speech failures are easy to miss in a quick listen but jarring on repeated use — mispronounced names, numbers read in an unnatural order ("$1,200" read digit-by-digit instead of as an amount), or acronyms that get spelled out when they should be spoken as a word, or vice versa. These need a dedicated test set built from the actual content the system will read aloud, not generic sample sentences, since pronunciation errors cluster heavily around domain-specific terms, proper nouns, and numeric formats.

Prosody and naturalness are harder to test automatically and usually need a structured human-listening review — is the pacing appropriate for the content, does the intonation match the punctuation (a question should sound like a question), and does long-form audio maintain a consistent voice rather than drifting in tone or speed partway through. For products offering multiple voices or languages, per-voice and per-language regression testing matters, since a fix to one voice's pronunciation model can silently regress another.