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Beyond Word Error Rate: A Switch Aware Evaluation of ASR and Audio Language Models on English Yoruba Code-Switched Speech

arxiv.org/abs/2609.11786

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Updated 6 h ago · first seen 11 Sept 2026

paper_01M294G4W65KNQ5C047H4CV934

Published
11 Sept 2026
T1 · 6 h ago
arXiv
2609.11786
T1 · 6 h ago
Category
cs.CL
T1 · 6 h ago

Abstract

Automatic speech recognition (ASR) systems and audio language models (audio LMs) now report low error rates on monolingual benchmarks, but their behavior on code switched speech in low resource, diacritic rich languages remains poorly characterized. We present a switch aware evaluation of eleven modern systems (six ASR models and five audio LMs) on English Yoruba code-switched speech, using a deterministic 2000 utterance evaluation set and a shared scoring pipeline. Beyond word error rate (WER), we report switch localized diagnostics: a switch entry token error rate (SETER), windowed switch point error rates, language specific error rates, and a diacritic insensitive WER. Our central finding is that aggregate WER hides code switching behavior. The best system by WER (an ASR model) is statistically indistinguishable from a leading audio LM on WER, yet the audio LM is significantly better on every switch localized metric. Across faithful systems, Yoruba token recognition collapses (error 0.97 for almost all systems) while English tokens are recognized far better, and errors concentrate sharply at switches into Yoruba. Several generative audio LMs fail as exact transcribers, producing translation, verbosity, and prompt leakage that are strongly prompt dependent. We release manifests, metric implementations, and evaluation scripts to support reproducible, switch aware benchmarking for African code switched speech.

Authors 2

Chibuzor Okocha, Christan Earl Grant

Specification

Official page

Source:arXiv (Atom API + RSS)T1observed 6 h agohigh

Arxiv announce type
new

Source:arXiv (Atom API + RSS)T1observed 6 h agohigh

arXiv id
2609.11786

Source:arXiv (Atom API + RSS)T1observed 6 h agohigh

Categories
cs.CL, cs.AI

Source:arXiv (Atom API + RSS)T1observed 6 h agohigh

PDF

Source:arXiv (Atom API + RSS)T1observed 6 h agohigh

Primary category
cs.CL

Source:arXiv (Atom API + RSS)T1observed 6 h agohigh

Published
11 Sept 2026

Source:arXiv (Atom API + RSS)T1observed 6 h agohigh

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Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

6 h ago

Conflicts

None