What Counts as an Error? Dual-Reference Benchmarking for Atypical ASR
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Computer Science > Computation and Language
Title:What Counts as an Error? Dual-Reference Benchmarking for Atypical ASR
Abstract:ASR systems have been often reported to underperform on atypical speech. An often conflated compounding factor is the existence of two valid transcription references: verbatim (actual produced speech, including repetitions/prolongations) and intended (the canonical form of the text with disfluencies removed) in atypical speech recognition depending on context and use-case. Most ASR evaluations conflate this duality into a single ground truth and reward systems that delete disfluencies, ignoring verbatim faithfulness. We benchmark 11 ASR models from encoder-decoder, CTC and transducer families using both verbatim and intended references on atypical stuttered speech as a case study. Our quantitative assessment underlines the disparity in model performance and rankings using the two transcript styles. Through this analysis, we highlight the importance of selecting a suitable transcription reference for valid model selection depending on the use-case, particularly for atypical ASR.
| Comments: | 5 pages, 2 figures, accepted at Interspeech 2026 |
| Subjects: | Computation and Language (cs.CL); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2606.31112 [cs.CL] |
| (or arXiv:2606.31112v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.31112
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Hawau Olamide Toyin [view email][v1] Tue, 30 Jun 2026 04:15:39 UTC (161 KB)
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