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arXiv · 2606.10439

Enhancing Multilingual LLM-based ASR with Mixture of Experts and Dynamic Downsampling

Guodong Lin·Ziqi Chen·Yuxiang Fu·Ke Li·Wei-Qiang Zhang
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Abstract

The rapid progress of large language models (LLMs) has opened up a new frontier for automatic speech recognition (ASR), making their effective integration a critical and challenging research direction. To this end, this work proposes a projector-based LLM-ASR framework targeting the key challenges of multilingual generalization and modality alignment. Our approach incorporates a Mixture of Experts (MoE) architecture to improve cross-lingual adaptability, and a Continuous Integrate-and-Fire (CIF) mechanism for dynamic downsampling and modality alignment. Experimental results show that the combination of these components yields substantial performance improvements, surpassing strong baseline models. The proposed method represents a step toward building more accurate, robust, and generalizable LLM-based ASR systems.

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Guodong Lin, Ziqi Chen, Yuxiang Fu, Ke Li, Wei-Qiang Zhang. 2026-06-09. Enhancing Multilingual LLM-based ASR with Mixture of Experts and Dynamic Downsampling. https://doi.org/10.1109/icassp55912.2026.11464266

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