@inproceedings{gupta-etal-2026-cose,
title = "{C}o{SE}-{E}: A Benchmark for Code-switched Speech Evaluation in Enterprise Settings",
author = "Gupta, Shama and
Nguyen, Hoang H and
Huang, Chelsea and
Brin, Lindsay Devon and
Riols, Fanny",
editor = "T.Y.S.S., Santosh and
Ghosh, Sreyan and
Duraiswami, Ramani and
Watanabe, Shinji and
Arora, Siddhant and
Kumar, Sonal and
Adi, Yossi and
Manocha, Dinesh and
Garcia Perera, Leibny Paola",
booktitle = "The Proceedings of Speech and Audio Language Models Workshop (2nd Edition)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.salma-1.11/",
pages = "156--179",
ISBN = "979-8-89176-496-5",
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 CS-ASR evaluation for multilingual voice agents in enterprise deployment."
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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 CS-ASR evaluation for multilingual voice agents in enterprise deployment.</abstract>
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%0 Conference Proceedings
%T CoSE-E: A Benchmark for Code-switched Speech Evaluation in Enterprise Settings
%A Gupta, Shama
%A Nguyen, Hoang H.
%A Huang, Chelsea
%A Brin, Lindsay Devon
%A Riols, Fanny
%Y T.Y.S.S., Santosh
%Y Ghosh, Sreyan
%Y Duraiswami, Ramani
%Y Watanabe, Shinji
%Y Arora, Siddhant
%Y Kumar, Sonal
%Y Adi, Yossi
%Y Manocha, Dinesh
%Y Garcia Perera, Leibny Paola
%S The Proceedings of Speech and Audio Language Models Workshop (2nd Edition)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-496-5
%F gupta-etal-2026-cose
%X 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 CS-ASR evaluation for multilingual voice agents in enterprise deployment.
%U https://aclanthology.org/2026.salma-1.11/
%P 156-179
Markdown (Informal)
[CoSE-E: A Benchmark for Code-switched Speech Evaluation in Enterprise Settings](https://aclanthology.org/2026.salma-1.11/) (Gupta et al., SALMA 2026)
ACL