AfriSwitch：用于野外非洲语码转换语音识别的基准测试集

中文摘要 AI 辅助

语码转换在双语非洲对话中十分普遍，但大多数自动语音识别（ASR）系统假设输入为单语言，并在精心整理的单语言基准上进行评估。我们推出AfriSwitch，这是一个时长61.36小时的野外语码转换语音人工转录基准，涵盖16种非洲语言及语言变体，附带语码转换层级的英语片段标签、每句话的语码混合指数（CMI）和转换点数量。语料库统计显示，非洲语言的语码转换行为在两个基本独立的维度上差异显著：说话人转换的频繁程度，以及混合的均衡程度。没有单一标量能准确衡量一种语言的语码转换程度。对5个开源及商用多语言ASR系统进行零样本基准测试，结果显示其词错误率（WER）远高于同语言已发表的单语言结果，最优系统平均WER为35.93%，且无任何系统在任一种语言上的WER低于24%。针对非洲的定向训练，而非模型规模或名义上的语言覆盖度，最能预测系统性能。

英文摘要

Code-switching is pervasive in bilingual African conversation, yet most ASR systems assume monolingual input and are evaluated on curated monolingual benchmarks. We present AfriSwitch, a 61.36-hour human-transcribed benchmark of in-the-wild code-switched speech spanning 16 African languages and language varieties, released with switch-level English span tags, perutterance Code-Mixing Index (CMI), and switch-point counts. Corpus statistics show that mixing behaviour varies widely across African languages along two largely independent axes: how often speakers alternate, and how balanced the mixture is. No single scalar captures how code-switched a language is. Benchmarking five open and commercial multilingual ASR systems zero-shot yields word error rates far above published monolingual figures for the same languages, with the best system averaging 35.93% WER and no system falling below 24% on any language. Africa-targeted training, not model scale or nominal language coverage, best predicts performance.

发表机构

• Intron Health

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