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arXiv · 2606.21990

Adding Robust Code-Switching Capabilities to High Performance Multilingual ASR

Enes Yavuz Ugan·Alexander Waibel
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Abstract

Code-switching (CSW) remains challenging for large multi-lingual ASR systems in real-world deployment. While fine-tuning on synthetic CSW data is possible, it generally degrades strong monolingual baselines. Our goal is to preserve these capabilities while extending models to handle complex code-switching, including morphological variations across languages. We propose Bayesian factorized adaptation, which learns to efficiently integrate switching-relevant knowledge into strong pretrained models without overwriting existing capabilities. Requiring only a small amount of synthetic data, our approach reduces transcription errors by 32.87% on code-switched words while improving overall WER by 5.31%, all while maintaining mono-lingual performance. Our results demonstrate that effective CSW adaptation depends more on knowledge integration than data complexity.

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Enes Yavuz Ugan, Alexander Waibel. 2026-06-20. Adding Robust Code-Switching Capabilities to High Performance Multilingual ASR. https://arxiv.org/abs/2606.21990

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