MOSA: Mixtures of Simple Adapters Outperform Monolithic Approaches in LLM-based Multilingual ASR

Abstract

LLM-based ASR overcomes multilingual data scarcity by projecting speech representations into the LLM space to leverage its robust semantic and reasoning capabilities. However, while previous approaches typically enhance performance by scaling data or model parameters, a single projector often struggles to effectively align representations across different languages. In this work, we propose an MoE-based projector named MOSA (Mixture of Simple Adapters). By aggregating multiple simple adapters, this architecture enables different experts to specialize in learning either language-shared or language-specific knowledge. This approach not only mitigates parameter interference between languages but also facilitates positive transfer from high-resource to low-resource languages, effectively alleviating data scarcity issues. Experimental results demonstrate that MOSA-Base achieves a 15.4% relative reduction in average WER compared to the Ideal-LLM Base, consistently outperforming it across all languages. Notably, MOSA achieves a 13.3% WER reduction over the Ideal-LLM Base while utilizing only 60% of its parameters. These findings highlight MOSA's superior parameter efficiency and robustness against data imbalance, suggesting that a mixture of simple adapters is more suitable for multilingual LLM-based ASR than complex single-adapter designs.