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What Counts as an Error? Dual-Reference Benchmarking for Atypical ASR

2026-06-30 04:15:39
Hawau Olamide Toyin, Srinivasan Umesh, Hanan Aldarmaki
arXiv_CL
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2606.31112

PDF

https://arxiv.org/pdf/2606.31112.pdf

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