{"schema":"record/v1","id":"art_01M5MSEKTJMAV9R5V0DJVGRA3X","type":"article","revision":1,"etag":"\"r1.s0\"","title":"How is word error rate defined, what does the published critique say about it, and how much does the choice of text normaliser (for example Whisper's) change what is counted?","summary":"WER counts substitutions, deletions and insertions against the reference word count, so it can exceed 1 (upper bound max(N1,N2)/N1). Morris et al. (2004) argue that measures of information communicated suit most applications better. WER also depends on text normalisation. Whisper's English and basic normalisers delete bracketed and parenthesised text (so tags like [music] or (laughs) vanish) and standardise numbers and spellings before scoring. WhisperX reports insertion error rate and 5-gram duplicates to catch hallucinated repetition. WER says nothing about timing, speaker labels or punctuation.","status":"active","body":{"text":"WER counts substitutions, deletions and insertions against the reference word count, so it can exceed 1 (upper bound max(N1,N2)/N1). Morris et al. (2004) argue that measures of information communicated suit most applications better. WER also depends on text normalisation. Whisper's English and basic normalisers delete bracketed and parenthesised text (so tags like [music] or (laughs) vanish) and standardise numbers and spellings before scoring. WhisperX reports insertion error rate and 5-gram duplicates to catch hallucinated repetition. WER says nothing about timing, speaker labels or punctuation.\n\nCurator qualifications: Papers and code read 2026-10-22; no listening test or ASR scored.\n\nLimitations: Papers and code read 2026-10-22; no listening test or ASR scored.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/pdf/2303.00747","note":"Morris, Maier, Green, Interspeech 2004: abstract and equation (2); whisper normalizers/english.py @8609812 L14, L531-532; whisper normalizers/basic.py @8609812 L69-70; WhisperX paper (arXiv 2303.00747) Section 3.2"},{"url":"https://www.isca-archive.org/interspeech_2004/morris04_interspeech.pdf","note":"Morris, Maier, Green, Interspeech 2004: abstract and equation (2); whisper normalizers/english.py @8609812 L14, L531-532; whisper normalizers/basic.py @8609812 L69-70; WhisperX paper (arXiv 2303.00747) Section 3.2"},{"url":"https://github.com/openai/whisper/blob/86098128c0b4f24f0e2aa2994de830614b474227/whisper/normalizers/english.py","note":"Morris, Maier, Green, Interspeech 2004: abstract and equation (2); whisper normalizers/english.py @8609812 L14, L531-532; whisper normalizers/basic.py @8609812 L69-70; WhisperX paper (arXiv 2303.00747) Section 3.2"},{"url":"https://github.com/openai/whisper/blob/86098128c0b4f24f0e2aa2994de830614b474227/whisper/normalizers/basic.py","note":"Morris, Maier, Green, Interspeech 2004: abstract and equation (2); whisper normalizers/english.py @8609812 L14, L531-532; whisper normalizers/basic.py @8609812 L69-70; WhisperX paper (arXiv 2303.00747) Section 3.2"}]},"categories":["T13","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/pdf/2303.00747","claim":"WER counts substitutions, deletions and insertions against the reference word count, so it can exceed 1 (upper bound max(N1,N2)/N1). Morris et al. (2004) argue that measures of information communicated suit most applications better. WER also depends on text normalisation. Whisper's English and basic normalisers delete bracketed and parenthesised text (so tags like [music] or (laughs) vanish) and standardise numbers and spellings before scoring. WhisperX reports insertion error rate and 5-gram duplicates to catch hallucinated repetition. 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Morris et al. (2004) argue that measures of information communicated suit most applications better. WER also depends on text normalisation. Whisper's English and basic normalisers delete bracketed and parenthesised text (so tags like [music] or (laughs) vanish) and standardise numbers and spellings before scoring. WhisperX reports insertion error rate and 5-gram duplicates to catch hallucinated repetition. WER says nothing about timing, speaker labels or punctuation.\n\nCurator qualifications: Papers and code read 2026-10-22; no listening test or ASR scored.\n\nLimitations: Papers and code read 2026-10-22; no listening test or ASR scored.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/pdf/2303.00747","note":"Morris, Maier, Green, Interspeech 2004: abstract and equation (2); whisper normalizers/english.py @8609812 L14, L531-532; whisper normalizers/basic.py @8609812 L69-70; WhisperX paper (arXiv 2303.00747) Section 3.2"},{"url":"https://www.isca-archive.org/interspeech_2004/morris04_interspeech.pdf","note":"Morris, Maier, Green, Interspeech 2004: abstract and equation (2); whisper normalizers/english.py @8609812 L14, L531-532; whisper normalizers/basic.py @8609812 L69-70; WhisperX paper (arXiv 2303.00747) Section 3.2"},{"url":"https://github.com/openai/whisper/blob/86098128c0b4f24f0e2aa2994de830614b474227/whisper/normalizers/english.py","note":"Morris, Maier, Green, Interspeech 2004: abstract and equation (2); whisper normalizers/english.py @8609812 L14, L531-532; whisper normalizers/basic.py @8609812 L69-70; WhisperX paper (arXiv 2303.00747) Section 3.2"},{"url":"https://github.com/openai/whisper/blob/86098128c0b4f24f0e2aa2994de830614b474227/whisper/normalizers/basic.py","note":"Morris, Maier, Green, Interspeech 2004: abstract and equation (2); whisper normalizers/english.py @8609812 L14, L531-532; whisper normalizers/basic.py @8609812 L69-70; WhisperX paper (arXiv 2303.00747) Section 3.2"}]},"tags":[],"type":"article","links":[],"title":"How is word error rate defined, what does the published critique say about it, and how much does the choice of text normaliser (for example Whisper's) change what is counted?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"WER counts substitutions, deletions and insertions against the reference word count, so it can exceed 1 (upper bound max(N1,N2)/N1). 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