Driven by large scale datasets and LLM based architectures, automatic speech recognition (ASR) systems have achieved remarkable improvements in accuracy. However, challenges persist for domain-specific terminology, and short utterances lacking semantic coherence, where recognition performance often degrades significantly. In this work, we present Index-MSR, an efficient multimodal speech recognition framework. At its core is a novel Multimodal Fusion Decoder (MFD), which effectively incorporates text-related information from videos (e.g., subtitles and presentation slides) into the speech recognition. This cross-modal integration not only enhances overall ASR accuracy but also yields substantial reductions in substitution errors. Extensive evaluations on both an in-house subtitle dataset and a public AVSR dataset demonstrate that Index-MSR achieves sota accuracy, with substitution errors reduced by 20,50%. These results demonstrate that our approach efficiently exploits text-related cues from video to improve speech recognition accuracy, showing strong potential in applications requiring strict audio text synchronization, such as audio translation.
@article{arxiv.2509.22744,
title = {Index-MSR: A high-efficiency multimodal fusion framework for speech recognition},
author = {Jinming Chen and Lu Wang and Zheshu Song and Wei Deng},
journal= {arXiv preprint arXiv:2509.22744},
year = {2025}
}