Advancing Multi-talker ASR Performance with Large Language Models

• URL: http://arxiv.org/abs/2408.17431v1
• Date: Fri, 30 Aug 2024 17:29:25 GMT
• Title: Advancing Multi-talker ASR Performance with Large Language Models
• Authors: Mohan Shi, Zengrui Jin, Yaoxun Xu, Yong Xu, Shi-Xiong Zhang, Kun Wei, Yiwen Shao, Chunlei Zhang, Dong Yu,
• Abstract summary: Recognizing overlapping speech from multiple speakers in conversational scenarios is one of the most challenging problem for automatic speech recognition (ASR) In this paper, we propose an LLM-based SOT approach for multi-talker ASR, leveraging pre-trained speech encoder and LLM. Our approach surpasses traditional AED-based methods on the simulated dataset LibriMix and achieves state-of-the-art performance on the evaluation set of the real-world dataset AMI.
• Score: 48.52252970956368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recognizing overlapping speech from multiple speakers in conversational scenarios is one of the most challenging problem for automatic speech recognition (ASR). Serialized output training (SOT) is a classic method to address multi-talker ASR, with the idea of concatenating transcriptions from multiple speakers according to the emission times of their speech for training. However, SOT-style transcriptions, derived from concatenating multiple related utterances in a conversation, depend significantly on modeling long contexts. Therefore, compared to traditional methods that primarily emphasize encoder performance in attention-based encoder-decoder (AED) architectures, a novel approach utilizing large language models (LLMs) that leverages the capabilities of pre-trained decoders may be better suited for such complex and challenging scenarios. In this paper, we propose an LLM-based SOT approach for multi-talker ASR, leveraging pre-trained speech encoder and LLM, fine-tuning them on multi-talker dataset using appropriate strategies. Experimental results demonstrate that our approach surpasses traditional AED-based methods on the simulated dataset LibriMix and achieves state-of-the-art performance on the evaluation set of the real-world dataset AMI, outperforming the AED model trained with 1000 times more supervised data in previous works.

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