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They estimate words from the recording and may also add punctuation or timestamps. A transcript is a model output that can contain omissions, substitutions, or added words, so important details need review against the audio.

Key takeaways

• Evaluate the intended languages and recording conditions.
• Document scoring normalization.
• Review critical details against the audio.

Specify the language, audio format, and expected recording conditions. Background noise, overlapping speakers, unusual names, and domain-specific terminology can affect recognition. A system’s performance on one dataset does not establish the same result for every accent or environment. Separate transcription from speaker identification, translation, and summarization. Those tasks may be combined in a product, but each can introduce additional errors. A speaker label is not necessarily a verified identity. Word error rate compares substitutions, deletions, and insertions with a reference transcript. Normalization rules for punctuation, casing, and tokenization affect the result. Report those rules and inspect meaning-changing errors rather than relying solely on one aggregate percentage. Preserve access to the original recording and relevant timestamps where permitted. Provide a review process for names, numbers, technical terms, and uncertain passages. Test silence and non-speech audio so the system does not turn an absence of speech into a confident-looking transcript.

1. Use an invented reference transcript containing 100 words. The recognized transcript has four substitutions, three deletions, and two insertions.
2. Word error rate is (4+3+2)/100 = 9%.
3. Review which words changed. The percentage alone does not reveal whether the mistakes altered a key instruction or merely a filler phrase.

The constructed arithmetic explains the metric without claiming a result for any speech-recognition product.