Computer Science > Computation and Language
arXiv:2502.18434 (cs)
[Submitted on 25 Feb 2025]
Title:Exploring Gender Disparities in Automatic Speech Recognition Technology
View a PDF of the paper titled Exploring Gender Disparities in Automatic Speech Recognition Technology, by Hend ElGhazaly and 3 other authors
View PDFHTML (experimental)Abstract: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.
| Comments: | ISCA/ITG Workshop on Diversity in Large Speech and Language Models |
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2502.18434 [cs.CL] |
| (orarXiv:2502.18434v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2502.18434 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Exploring Gender Disparities in Automatic Speech Recognition Technology, by Hend ElGhazaly and 3 other authors
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