Speech-to-Text Testing
Speech-to-text testing measures transcription accuracy (via word error rate) across the accents, background noise levels, speaking speeds, and audio quality a system will actually face in production, along with latency for real-time use cases and correct handling of domain-specific vocabulary, names, and jargon.
Word error rate on a clean, single-speaker studio recording is close to meaningless as a production readiness signal — the gap between that number and real-world accuracy (background noise, overlapping speakers, accents underrepresented in training data, phone-call audio compression) is usually the difference that determines whether a feature actually works. A representative test set needs to be built from audio that matches real usage conditions, not the easiest case.
Domain vocabulary is a frequent, specific failure point — a general-purpose speech-to-text model will reliably mis-transcribe product names, technical jargon, and person names it hasn't seen, which matters a lot for a support-call transcription feature and not at all for a generic dictation app. Testing this means building a custom vocabulary test set from the actual domain, not just general benchmark audio. For real-time use cases, latency (time from spoken word to transcribed text) needs its own test track separate from accuracy, since a system can be highly accurate but too slow to feel responsive in a live conversation.
