Recent advances in LLM-based ASR connect frozen speech encoders with Large Language Models (LLMs) via lightweight projectors. While effective in monolingual settings, a single projector struggles to capture the diverse acoustic-to-semantic mappings required for multilingual ASR. To address this, we propose SMEAR-MoE, a stabilized Mixture-of-Experts projector that ensures dense gradient flow to all experts, preventing expert collapse while enabling cross-lingual sharing. We systematically compare monolithic, static multi-projector, and dynamic MoE designs across four Indic languages (Hindi, Marathi, Tamil, Telugu). Our SMEAR-MoE achieves strong performance, delivering upto a 7.6% relative WER reduction over the single-projector baseline, while maintaining comparable runtime efficiency. Analysis of expert routing further shows linguistically meaningful specialization, with related languages sharing experts. These results demonstrate that stable multi-expert projectors are key to scalable and robust multilingual ASR.

翻译：近期基于大语言模型（LLM）的自动语音识别（ASR）系统通过轻量级投影器将冻结的语音编码器与大语言模型（LLMs）相连接。该方法在单语言场景下表现良好，但单一投影器难以捕捉多语言ASR所需的多样化声学-语义映射关系。为此，我们提出SMEAR-MoE——一种稳定的专家混合（Mixture-of-Experts）投影器，该设计确保所有专家均获得密集梯度流，在防止专家坍缩的同时实现跨语言知识共享。我们系统比较了单体式、静态多投影器及动态MoE三种架构在四种印度语言（Hindi、Marathi、Tamil、Telugu）上的表现。实验表明，SMEAR-MoE在保持相近运行效率的同时，相比单投影器基线实现了最高7.6%的相对词错误率（WER）降低。专家路由分析进一步揭示了语言学意义上的专业化分工，相关语言会共享专家资源。这些结果证明，稳定的多专家投影器是实现可扩展且鲁棒的多语言ASR系统的关键。