AfriSwitch: A Benchmark for In-the-Wild African Code-Switched Speech Recognition
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Computer Science > Computation and Language
Title:AfriSwitch: A Benchmark for In-the-Wild African Code-Switched Speech Recognition
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-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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.26434 [cs.CL] |
| (or arXiv:2608.26434v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.26434
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Gabrial Zencha Ashungafac [view email][v1] Wed, 26 Aug 2026 22:20:58 UTC (940 KB)
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