Index-MSR: A high-efficiency multimodal fusion framework for speech recognition

• URL: http://arxiv.org/abs/2509.22744v1
• Date: Fri, 26 Sep 2025 03:47:15 GMT
• Title: Index-MSR: A high-efficiency multimodal fusion framework for speech recognition
• Authors: Jinming Chen, Lu Wang, Zheshu Song, Wei Deng,
• Abstract summary: Index-MSR is an efficient multimodal speech recognition framework.<n>MFD effectively incorporates text-related information from videos into the speech recognition.<n>We show that Index-MSR achieves sota accuracy, with substitution errors reduced by 20,50%.
• Score: 7.677016652056559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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