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

Paper recorded by Signals 4 on 2026-09-10 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-10 on arXiv · recorded by Signals 4 on 2026-09-11

Category: cs.AI · 人工智能 · first seen 2026-09-11

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 dete

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#118 most recent of 300 cs.AI papers we have recorded · ↑ newer: Thinking with Looped Flows · ↓ older: Recognizing Is Not Reversing: A Controlled Inversion Test of Fact-Pres
Cite this page: Beyond Word Error Rate: A Switch Aware Evaluation of ASR and Audio Language Models on English Yoruba Code-Switched Speech: the #118 most recent of 300 cs.AI papers we have recorded (as of 2026-09-10). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/beyond-word-error-rate-a-switch-aware-evaluation-of-asr-and-audio-language-model.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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