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

Shama Gupta, Hoang H Nguyen, Chelsea Huang, Lindsay Devon Brin, Fanny Riols
9/28/2026
cs.SDcs.CLcs.LG

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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.

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Cite this paper

@article{gupta2026cosee,
  title  = {CoSE-E: A Benchmark for Code-switched Speech Evaluation in Enterprise Settings},
  author = {Shama Gupta and Hoang H Nguyen and Chelsea Huang and Lindsay Devon Brin and Fanny Riols},
  year   = {2026},
  eprint = {2609.35645},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2609.35645v1}
}

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