[2605.23975] Direct Preference Optimization for English-Mandarin Code-Switching Speech Recognition in Audio LLMs
[Submitted on 13 May 2026]
Title:Direct Preference Optimization for English-Mandarin Code-Switching Speech Recognition in Audio LLMs
View a PDF of the paper titled Direct Preference Optimization for English-Mandarin Code-Switching Speech Recognition in Audio LLMs, by Trung Nguyen Quang and 5 other authors
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Abstract:Audio large language models (Audio LLMs) exhibit systematic failures in transcribing code-switching speech despite strong multilingual capabilities. Focusing on English-Mandarin, we identify three failure modes: language omission, translation-instead-of-transcription, and hallucination. We apply Direct Preference Optimization (DPO) to align models, constructing preference pairs in which chosen responses preserve mixed-language content while rejected responses mimic failure patterns. Training three Audio LLMs on 100K pairs (570 hours), we observe consistent behavioral shifts: models learn to preserve language composition rather than translating when prompted for transcription. This alignment yields MER reductions up to 89.6% (in-distribution) and 20.0% (out-of-distribution). Our findings suggest DPO can effectively elicit correct code-switching transcription behavior from multilingual Audio LLMs.
Subjects:
Computation and Language (cs.CL); Sound (cs.SD)
Cite as: arXiv:2605.23975 [cs.CL]
(or arXiv:2605.23975v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.23975
arXiv-issued DOI via DataCite
Submission history
From: Trung Nguyen Quang [view email] [v1] Wed, 13 May 2026 16:15:33 UTC (25 KB)
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