Towards measuring fairness in speech recognition: Fair-Speech dataset

• URL: http://arxiv.org/abs/2408.12734v1
• Date: Thu, 22 Aug 2024 20:55:17 GMT
• Title: Towards measuring fairness in speech recognition: Fair-Speech dataset
• Authors: Irina-Elena Veliche, Zhuangqun Huang, Vineeth Ayyat Kochaniyan, Fuchun Peng, Ozlem Kalinli, Michael L. Seltzer,
• Abstract summary: This paper introduces a novel dataset, Fair-Speech, a publicly released corpus to help researchers evaluate their ASR models for accuracy across a diverse set of self-reported demographic information. Our dataset includes approximately 26.5K utterances in recorded speech by 593 people in the United States, who were paid to record and submit audios of themselves saying voice commands.
• Score: 14.703638352216132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The current public datasets for speech recognition (ASR) tend not to focus specifically on the fairness aspect, such as performance across different demographic groups. This paper introduces a novel dataset, Fair-Speech, a publicly released corpus to help researchers evaluate their ASR models for accuracy across a diverse set of self-reported demographic information, such as age, gender, ethnicity, geographic variation and whether the participants consider themselves native English speakers. Our dataset includes approximately 26.5K utterances in recorded speech by 593 people in the United States, who were paid to record and submit audios of themselves saying voice commands. We also provide ASR baselines, including on models trained on transcribed and untranscribed social media videos and open source models.

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