[Submitted on 10 Sep 2026]
Title:Quantifying Consonant Contributions to Word Intelligibility via Acoustic Masking
View a PDF of the paper titled Quantifying Consonant Contributions to Word Intelligibility via Acoustic Masking, by Eunjung Yeo and 6 other authors
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Abstract:Consonants contribute unequally to whether a word is understood. Given the limited time available for therapy, ranking consonants by contribution to intelligibility helps prioritize intervention targets in motor speech disorders. However, measuring this contribution relies on perceptual studies that are difficult to scale. This paper presents a scalable method that measures consonant contribution using acoustic masking. We silence one consonant at a time in an isolated word and test whether an automatic speech recognition (ASR) model still recognizes the word. We define a consonant's contribution score as the proportion of its masked instances for which the word becomes misrecognized, which we refer to as the mask-induced misrecognition rate (MMR). We validate MMR against two linguistic factors previously reported to correlate with consonant contribution, namely phoneme frequency and functional load. We apply this analysis across four languages, English, Spanish, German, and Czech, using three ASR architectures, MMS (encoder-only), Whisper (encoder-decoder), and Qwen3-ASR (LLM-based). Using partial Spearman correlations, we find that phoneme frequency correlates negatively with MMR while functional load correlates positively. In other words, more frequent consonants are less disruptive when masked, whereas consonants carrying more lexical contrast are more disruptive. Further cross-language analysis shows that consonant rankings are not consistent, indicating that consonant contribution is language-dependent.
Comments: 7 pages, 5 figures, Accepted to SLT 2026
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2609.12122 [cs.CL]
(or arXiv:2609.12122v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.12122
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Eunjung Yeo [view email] [v1] Thu, 10 Sep 2026 18:49:21 UTC (393 KB)
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