Claytone Sikasote, Hussein Suleman, Jan Buys · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26821 · open-access PDF
尽管自动语音识别系统(ASR)取得了进展,但目前的系统表现出扬声器属性偏见,即他们的语音识别性能是基于他们的人口特征,如性别,口音和扬声器类型对某些扬声器的偏见。
Machine translation (Meta M2M100); the English original follows.
Despite advancements in automatic speech recognition systems (ASR), current systems exhibit speaker-attribute bias, i.e., their speech recognition performance is biased against certain speakers based on their demographic attributes such as gender, accents, and speaker type. However, accurately assessing speaker attribute bias in a given ASR system requires the availability of a bias evaluation dataset with comparable speech across subgroups of a speaker attribute. In this paper, we introduce African speech Bias Evaluation Dataset (AfriBED), a novel speech recognition bias evaluation dataset fo
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