Improving ASR Fairness for Cleft Lip and Palate Speech: A Study on Severity-Aware Data Mixing

Abstract

Speech produced by individuals with cleft lip and palate (CLP) is often hypernasal (and sometimes breathy) due to structural anomalies, yielding shifts in formant structure that degrade automatic speech recognition (ASR) performance and fairness. Building on evidence that mainstream ASR systems underperform on atypical and disordered speech, we posit that widely used services (e.g., Google Speech-to-Text) exhibit reduced fairness for CLP speech, and we evaluate this claim empirically. To quantify fairness consistently, we introduce a simple fairness score (FS) that trades off overall error and between-group disparity. Despite formant disruptions, mild and moderate CLP speech retains partial spectro-temporal alignment with typical speech, motivating the use of mixing strategies to improve fairness. We systematically investigated severity-aware mixing of CLP and normal speech at different severity levels and assessed its effect on fairness. Three ASR models GMM-HMM, Whisper, and XLSR were evaluated on the AIISH (Kannada language) and NMCPC (English language) datasets. A mixing strategy that leverages severity-aware mixing of CLP and normal speech improves fairness on both English (NMCPC) and Kannada (AIISH) corpora. Notably, the word error rate (WER) decreased from 37.58% to 25.47% (GMM-HMM, AIISH) and from 35.74% to 21.72% (Whisper, NMCPC).