Fairness of Automatic Speech Recognition in Cleft Lip and Palate Speech

TL;DR

This paper investigates the fairness of automatic speech recognition systems for cleft lip and palate speech, demonstrating that augmentation with normal speech improves recognition accuracy and fairness across different models and datasets.

Contribution

It systematically evaluates the impact of speech augmentation strategies on ASR fairness for CLP speech, highlighting improvements in word error rate and fairness scores.

Findings

Training with normal speech reduces WER for CLP speech.

Augmentation improves fairness scores significantly.

GMM-HMM performs best on AIISH dataset.

Abstract

Speech produced by individuals with cleft lip and palate (CLP) is often highly nasalized and breathy due to structural anomalies, causing shifts in formant structure that affect automatic speech recognition (ASR) performance and fairness. This study hypothesizes that publicly available ASR systems exhibit reduced fairness for CLP speech and confirms this through experiments. Despite formant disruptions, mild and moderate CLP speech retains some spectro-temporal alignment with normal speech, motivating augmentation strategies to enhance fairness. The study systematically explores augmenting CLP speech with normal speech across severity levels and evaluates its impact on ASR fairness. Three ASR models-GMM-HMM, Whisper, and XLS-R-were tested on AIISH and NMCPC datasets. Results indicate that training with normal speech and testing on mixed data improves word error rate (WER). Notably, WER decreased from $22.64\%$ to $18.76\%$ (GMM-HMM, AIISH) and $28.45\%$ to $18.89\%$ (Whisper, NMCPC). The superior performance of GMM-HMM on AIISH may be due to its suitability for Kannada children's speech, a challenge for foundation models like XLS-R and Whisper. To assess fairness, a fairness score was introduced, revealing improvements of $17.89\%$ (AIISH) and $47.50\%$ (NMCPC) with augmentation.