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arXiv · 2409.11889

M2R-Whisper: Multi-stage and Multi-scale Retrieval Augmentation for Enhancing Whisper

Jiaming Zhou·Shiwan Zhao·Jiabei He·Hui Wang·Wenjia Zeng·Yong Chen·Haoqin Sun·Aobo Kong·Yong Qin
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Abstract

State-of-the-art models like OpenAI's Whisper exhibit strong performance in multilingual automatic speech recognition (ASR), but they still face challenges in accurately recognizing diverse subdialects. In this paper, we propose M2R-whisper, a novel multi-stage and multi-scale retrieval augmentation approach designed to enhance ASR performance in low-resource settings. Building on the principles of in-context learning (ICL) and retrieval-augmented techniques, our method employs sentence-level ICL in the pre-processing stage to harness contextual information, while integrating token-level k-Nearest Neighbors (kNN) retrieval as a post-processing step to further refine the final output distribution. By synergistically combining sentence-level and token-level retrieval strategies, M2R-whisper effectively mitigates various types of recognition errors. Experiments conducted on Mandarin and subdialect datasets, including AISHELL-1 and KeSpeech, demonstrate substantial improvements in ASR accuracy, all achieved without any parameter updates.

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Jiaming Zhou, Shiwan Zhao, Jiabei He, Hui Wang, Wenjia Zeng, Yong Chen, Haoqin Sun, Aobo Kong, Yong Qin. 2024-09-18. M2R-Whisper: Multi-stage and Multi-scale Retrieval Augmentation for Enhancing Whisper. https://arxiv.org/abs/2409.11889

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