Exploring Gender Disparities in Automatic Speech Recognition Technology
Hend ElGhazaly, Bahman Mirheidari, Nafise Sadat Moosavi, Heidi Christensen · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · 2025
This study investigates factors influencing Automatic Speech Recognition (ASR) systems' fairness and performance across genders, beyond the conventional examination of demographics. Using the LibriSpeech dataset and the Whisper small model, we analyze how performance varies across different gender representations in training data. Our findings suggest a complex interplay between the gender ratio in training data and ASR performance. Optimal fairness occurs at specific gender distributions rather than a simple 50-50 split. Furthermore, our findings suggest that factors like pitch variability can significantly affect ASR accuracy. This research contributes to a deeper understanding of biases in ASR systems, highlighting the importance of carefully curated training data in mitigating gender bias.