Speech processing
Voice Activity Detection Benchmark: What to Measure
Evaluate VAD with missed speech, false alarms, boundary timing, and downstream transcription impact.
Voice activity detection decides which audio reaches the rest of a speech system. Missing the first half-second of a sentence can harm transcription even when the recognizer is excellent.
Annotate speech regions
Collect recordings with silence, background music, keyboard noise, quiet speech, interruptions, and overlap. Label speech start and end times with a consistent rule for breaths, laughter, and distant voices. Use a held-out set and keep its recording conditions visible in the report.
Measure detection and boundaries
Report speech miss rate and false-alarm rate by condition. Add boundary error: how early or late the detected segment begins and ends. Test several thresholds and show the tradeoff rather than choosing one threshold after looking at the final test set. The pyannote VAD pipeline exposes onset, offset, and minimum-duration controls and uses detection metrics, illustrating why configuration belongs in the report.
Check downstream effects
Run the same recognizer on audio segmented by each detector. Count clipped words and transcription errors caused by dropped regions. Measure delay for streaming calls as well as compute cost. Keep the segmentation rule and recognizer fixed so a VAD comparison is fair.