CoSE-E: A Benchmark for Code-switched Speech Evaluation in Enterprise Settings

Abstract:Code-switching (CS), a seamless alternation between languages within a single utterance, remains a critical challenge in automatic speech recognition (ASR). While prior works focus on conversational CS-ASR, enterprise settings demand evaluation of operational impact beyond edit-distance errors: how code-switching transcription errors propagate to downstream voice agent task failures. In this work, we propose (1) a CS-ASR synthetic benchmark and multidimensional evaluation framework tailored to enterprise domains, (2) systematic evaluation of frontier ASR systems across 5 language pairs, (3) diagnostic analysis of the additional transcription errors that code-switching introduces across language pairs and models. We release COSE-E to support enterprise-focused CSASR evaluation for multilingual voice agents in enterprise deployment.

Sound (cs.SD); Computation and Language (cs.CL); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)

arXiv:2609.35645 [cs.SD]

(or arXiv:2609.35645v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2609.35645 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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