Enhancing Multilingual LLM-based ASR with Mixture of Experts and Dynamic Downsampling
Guodong Lin, Ziqi Chen, Yuxiang Fu, Ke Li, Wei-Qiang Zhang
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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Classified with taxonomy v2 on Wed, 2 Sept 2026.