arXiv cs.CL· Claytone Sikasote, Hussein Suleman, Jan Buys·· 13 小时前AI 评分20
AfriBED：面向低资源非洲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
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研究者发布 AfriBED，一个针对低资源非洲语言 Bemba 和 Nyanja 的语音识别偏见评估数据集，用于检测和估计 ASR 系统的性别与说话人类型偏见。Bemba 集含 80 名说话人的 3928 条语音、共 9 小时，Nyanja 集含 127 名说话人的 6260 条语音、约 18 小时。基线实验显示该数据集可用于考察目标语言中的性别与说话人类型属性偏见。
正文 · 原文
Abstract: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.
| Comments: | Accepted at IEEE SLT 2026 Workshop Demo Track |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.26821 [cs.CL] |
| (or arXiv:2610.26821v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.26821 arXiv-issued DOI via DataCite |
Submission history
From: Claytone Sikasote [view email]
[v1]
Mon, 19 Oct 2026 17:53:32 UTC (240 KB)
[v2]
Tue, 20 Oct 2026 20:22:37 UTC (240 KB)
来源：arXiv cs.CL · arxiv.org