Quantifying Consonant Contributions to Word Intelligibility via Acoustic Masking

2026-09-10

AI Summary

本文通过声学掩蔽方法量化辅音对单词可理解性的贡献，使用自动语音识别模型评估掩蔽辅音后的单词识别情况，以解决言语障碍治疗中干预目标优先级的问题。

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.

Problem

• consonant contribution
• word intelligibility
• acoustic masking

Innovation

• acoustic masking
• mask-induced misrecognition rate (MMR)
• automatic speech recognition (ASR)