English(EN) AfriSwitch: A Benchmark for In-the-Wild African Code-Switched Speech Recognition

新的AfriSwitch基准测试突显了非洲语码转换语音识别的挑战

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员推出了AfriSwitch，这是一个旨在评估自动语音识别（ASR）系统在语码转换的非洲语言上的新基准测试。该基准测试包含16种非洲语言的61.36小时转录语音，并提供了诸如切换级别英语跨度标签和语码混合指数（CMI）等详细注释。对五个多语言ASR系统的初步测试显示，与单语性能相比，其词错误率（WER）显著更高，最佳系统的WER达到35.93%。研究结果表明，针对非洲语言进行专门训练，而不是仅仅依靠模型规模或广泛的语言覆盖范围，对于提高这些场景下的ASR性能至关重要。 AI

影响强调了需要专门的训练数据来提高非洲多样化语言的ASR性能。

排序理由该项目描述了一个新的语音识别基准测试，属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Gabrial Zencha Ashungafac, Busayo Awobade, Tobi Olatunji · 2026-08-28 04:00

AfriSwitch: 适用于野外非洲语码转换语音识别的基准测试

arXiv:2608.26434v1 Announce Type: new Abstract: 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-…

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AfriSwitch: 适用于野外非洲语码转换语音识别的基准测试

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