Scaling and Enhancing LLM-based AVSR: A Sparse Mixture of Projectors
  Approach
Authors: name: Umberto Cappellazzo, name: Minsu Kim, name: Stavros Petridis, name: Daniele Falavigna, name: Alessio Brutti

Audio-Visual Speech Recognition (AVSR) enhances robustness in noisy
environments by integrating visual cues. While recent advances integrate Large
Language Models (LLMs) into AVSR, their high computational cost hinders
deployment in resource-constrained settings. To address this, we propose
Llama-SMoP, an efficient Multimodal LLM that employs a Sparse Mixture of
Projectors (SMoP) module to scale model capacity without increasing inference
costs. By incorporating sparsely-gated mixture-of-experts (MoE) projectors,
Llama-SMoP enables the use of smaller LLMs while maintaining strong
performance. We explore three SMoP configurations and show that Llama-SMoP DEDR
(Disjoint-Experts, Disjoint-Routers), which uses modality-specific routers and
experts, achieves superior performance on ASR, VSR, and AVSR tasks. Ablation
studies confirm its effectiveness in expert activation, scalability, and noise
robustness.