Enhancing Multilingual LLM-based ASR with Mixture of Experts and Dynamic Downsampling
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Computer Science > Sound
Title:Enhancing Multilingual LLM-based ASR with Mixture of Experts and Dynamic Downsampling
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.
| Comments: | Accepted by ICASSP 2026 |
| Subjects: | Sound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2606.10439 [cs.SD] |
| (or arXiv:2606.10439v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10439
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | ICASSP (2026),18807-18811 |
| Related DOI: | https://doi.org/10.1109/ICASSP55912.2026.11464266
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