A Paradigm for Interpreting Metrics and Identifying Critical Errors in Automatic Speech Recognition
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866909014475931648 |
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| author | Bañeras-Roux, Thibault Rouvier, Mickael Wottawa, Jane Dufour, Richard |
| author_facet | Bañeras-Roux, Thibault Rouvier, Mickael Wottawa, Jane Dufour, Richard |
| contents | The most commonly used metrics for evaluating automatic speech transcriptions, namely Word Error Rate (WER) and Character Error Rate (CER), have been heavily criticized for their poor correlation to human perception and their inability to take into account linguistic and semantic information. While metric-based embeddings, seeking to approximate human perception, have been proposed, their scores remain difficult to interpret, unlike WER and CER. In this article, we overcome this problem by proposing a paradigm that consists in incorporating a chosen metric into it in order to obtain an equivalent of the error rate: a Minimum Edit Distance (minED). This approach parallels transcription errors with their human perception, also allowing an original study of the severity of these errors from a human perspective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03671 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Paradigm for Interpreting Metrics and Identifying Critical Errors in Automatic Speech Recognition Bañeras-Roux, Thibault Rouvier, Mickael Wottawa, Jane Dufour, Richard Computation and Language The most commonly used metrics for evaluating automatic speech transcriptions, namely Word Error Rate (WER) and Character Error Rate (CER), have been heavily criticized for their poor correlation to human perception and their inability to take into account linguistic and semantic information. While metric-based embeddings, seeking to approximate human perception, have been proposed, their scores remain difficult to interpret, unlike WER and CER. In this article, we overcome this problem by proposing a paradigm that consists in incorporating a chosen metric into it in order to obtain an equivalent of the error rate: a Minimum Edit Distance (minED). This approach parallels transcription errors with their human perception, also allowing an original study of the severity of these errors from a human perspective. |
| title | A Paradigm for Interpreting Metrics and Identifying Critical Errors in Automatic Speech Recognition |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.03671 |