Improving Contextual ASR via Multi-grained Fusion with Large Language Models

• URL: http://arxiv.org/abs/2507.12252v1
• Date: Wed, 16 Jul 2025 13:59:32 GMT
• Title: Improving Contextual ASR via Multi-grained Fusion with Large Language Models
• Authors: Shilin Zhou, Zhenghua Li,
• Abstract summary: We propose a novel multi-grained fusion approach that jointly leverages the strengths of both token-level and phrase-level fusion with Large Language Models (LLMs)<n>Our approach incorporates a late-fusion strategy that combines ASR's acoustic information with LLM's rich contextual knowledge, balancing fine-grained token precision with holistic phrase-level understanding.<n> Experiments on Chinese and English datasets demonstrate that our approach achieves state-of-the-art performance on keyword-related metrics.
• Score: 12.755830619473368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While end-to-end Automatic Speech Recognition (ASR) models have shown impressive performance in transcribing general speech, they often struggle to accurately recognize contextually relevant keywords, such as proper nouns or user-specific entities. Previous approaches have explored leveraging keyword dictionaries in the textual modality to improve keyword recognition, either through token-level fusion that guides token-by-token generation or phrase-level fusion that enables direct copying of keyword phrases. However, these methods operate at different granularities and have their own limitations. In this paper, we propose a novel multi-grained fusion approach that jointly leverages the strengths of both token-level and phrase-level fusion with Large Language Models (LLMs). Our approach incorporates a late-fusion strategy that elegantly combines ASR's acoustic information with LLM's rich contextual knowledge, balancing fine-grained token precision with holistic phrase-level understanding. Experiments on Chinese and English datasets demonstrate that our approach achieves state-of-the-art performance on keyword-related metrics while preserving high accuracy on non-keyword text. Ablation studies further confirm that the token-level and phrase-level components both contribute significantly to the performance gains, complementing each other in our joint multi-grained framework. The code and models will be publicly available at https://github.com/.

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