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New evaluation method reveals ASR/audio LM struggles with English-Yoruba code-switching

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces a switch-aware evaluation method for automatic speech recognition (ASR) and audio language models (audio LMs) when processing code-switched speech, specifically focusing on English and Yoruba. The study found that standard word error rate (WER) metrics can obscure significant performance differences in code-switching scenarios. The research highlights that while overall WER might be similar, models perform much worse on Yoruba segments and at the points where languages switch, with some generative models also exhibiting translation or verbosity issues. AI

IMPACT Highlights critical limitations in current ASR and audio LM performance on low-resource, code-switched languages, necessitating new evaluation standards.

RANK_REASON Research paper introducing a new evaluation methodology for ASR and audio LMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New evaluation method reveals ASR/audio LM struggles with English-Yoruba code-switching

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Research paper introducing a new evaluation methodology for ASR and audio LMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Chibuzor Okocha, Christan Earl Grant ·

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

    arXiv:2609.11786v1 Announce Type: new 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 poor…

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