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arXiv · 2406.09589

Multi-Channel Multi-Speaker ASR Using Target Speaker's Solo Segment

Yiwen Shao·Shi-Xiong Zhang·Yong Xu·Meng Yu·Dong Yu·Daniel Povey·Sanjeev Khudanpur
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Abstract

In the field of multi-channel, multi-speaker Automatic Speech Recognition (ASR), the task of discerning and accurately transcribing a target speaker's speech within background noise remains a formidable challenge. Traditional approaches often rely on microphone array configurations and the information of the target speaker's location or voiceprint. This study introduces the Solo Spatial Feature (Solo-SF), an innovative method that utilizes a target speaker's isolated speech segment to enhance ASR performance, thereby circumventing the need for conventional inputs like microphone array layouts. We explore effective strategies for selecting optimal solo segments, a crucial aspect for Solo-SF's success. Through evaluations conducted on the AliMeeting dataset and AISHELL-1 simulations, Solo-SF demonstrates superior performance over existing techniques, significantly lowering Character Error Rates (CER) in various test conditions. Our findings highlight Solo-SF's potential as an effective solution for addressing the complexities of multi-channel, multi-speaker ASR tasks.

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Yiwen Shao, Shi-Xiong Zhang, Yong Xu, Meng Yu, Dong Yu, Daniel Povey, Sanjeev Khudanpur. 2024-06-13. Multi-Channel Multi-Speaker ASR Using Target Speaker's Solo Segment. https://arxiv.org/abs/2406.09589

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