LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization

• URL: http://arxiv.org/abs/2409.00819v1
• Date: Sun, 1 Sep 2024 19:23:08 GMT
• Title: LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization
• Authors: Zengrui Jin, Yifan Yang, Mohan Shi, Wei Kang, Xiaoyu Yang, Zengwei Yao, Fangjun Kuang, Liyong Guo, Lingwei Meng, Long Lin, Yong Xu, Shi-Xiong Zhang, Daniel Povey,
• Abstract summary: We present a large-scale far-field overlapping speech dataset to advance research in speech separation, recognition, and speaker diarization. This dataset is a critical resource for decoding Who said What and When'' in multi-talker, reverberant environments.
• Score: 31.01716151301142
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The evolving speech processing landscape is increasingly focused on complex scenarios like meetings or cocktail parties with multiple simultaneous speakers and far-field conditions. Existing methodologies for addressing these challenges fall into two categories: multi-channel and single-channel solutions. Single-channel approaches, notable for their generality and convenience, do not require specific information about microphone arrays. This paper presents a large-scale far-field overlapping speech dataset, crafted to advance research in speech separation, recognition, and speaker diarization. This dataset is a critical resource for decoding ``Who said What and When'' in multi-talker, reverberant environments, a daunting challenge in the field. Additionally, we introduce a pipeline system encompassing speech separation, recognition, and diarization as a foundational benchmark. Evaluations on the WHAMR! dataset validate the broad applicability of the proposed data.

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