When Multiple Scripts Matter: Evaluating ASR in Clinical Settings
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Computer Science > Computation and Language
Title:When Multiple Scripts Matter: Evaluating ASR in Clinical Settings
Abstract:Automatic speech recognition (ASR) in non-English clinical settings is challenged by multiscript variability, where the same term may appear in multiple valid orthographic forms. Conventional string-matching evaluation metrics often underestimate ASR performance by treating orthographic variants as errors. To address this issue, we introduce MultiClin, a clinical ASR benchmark designed to evaluate robustness to multiscript variability. Experiments across diverse ASR models show that multiscript-aware evaluation provides a fairer assessment of recognition quality than conventional single-reference evaluation. We further investigate the impact of script consistency during training and find that inconsistent script mappings increase orthographic uncertainty and hinder model convergence, with a balanced 50% mapping ratio producing the highest entropy. In contrast, script unification consistently yields the best ASR performance. Our dataset and code are publicly available at: this https URL.
| Comments: | Interspeech 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.17826 [cs.CL] |
| (or arXiv:2606.17826v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.17826
arXiv-issued DOI via DataCite (pending registration)
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