Unverified50% confidenceFactExact time
Public word error rate (WER) figures come from benchmark datasets and cannot represent accuracy under a user's own accent, microphone, or industry terminology.
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Long-term
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10/18/2026
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UnverifiedWord error rate (WER) is the core metric for measuring speech transcription accuracy, calculated as the sum of substitutions, deletions and insertions divided by the total number of words in the reference text.76% similarUnverifiedWER对所有词等权对待，不区分关键信息词与填充词的错误，也无法衡量转写结果的可读性和语义准确性73% similarUnverifiedIn code-switching scenarios, the word error rate (WER) of current best-in-class speech recognition models remains between 15–25%, compared to within 5% for English monolingual scenarios.70% similarUnverifiedThe transcription quality bottleneck often lies at the ASR layer, where accents, technical terminology, and overlapping speech from multiple participants significantly reduce transcription accuracy.65% similarUnverifiedHuman annotators themselves are inconsistent, which sets a 'ceiling' for evaluating models; if even agreement among professional linguists can only reach a moderate level, then human-annotated data itself carries noise.65% similar
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