AfriBED：面向非洲低资源语言的语音识别偏见评估数据集
AfriBED: a Speech Recognition Bias Evaluation Dataset for Low Resource African Languages
一篇做语音识别公平性评估的数据集论文，专门针对 Bemba 和 Nyanja 这两种缺数据的非洲语言，做 ASR 偏见检测的可以看看。
论文发布了语音识别偏见评估数据集 AfriBED，覆盖 Bemba 和 Nyanja 两种非洲低资源语言。Bemba 子集包含 3928 条语音、约 9 小时录音，由 80 名说话人录制；Nyanja 子集包含 6260 条语音、约 18 小时录音，由 127 名说话人录制。数据集设计上保证说话人属性各子组之间的语音可比，用于检测 ASR 系统在性别和说话人类型上的偏见。基线实验验证了这两个数据集可以用于评估目标语言中的这类偏见。
AfriBED: a Speech Recognition Bias Evaluation Dataset for Low Resource African Languages
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 for two low resource African languages, Bemba and Nyanja, to detect and estimate gender and speaker-type bias for ASR systems. The dataset comprises 9 hours of speech with 3928 utterances recorded by 80 speakers for Bemba, while the Nyanja set includes 6260 utterances with approximately 18 hours of speech recorded by 127 speakers. The results of our baseline experiments demonstrate that the datasets are useful for examining gender and speaker-type attribute bias in the target languages.