LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization
<|reference_start|>LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization: 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.<|reference_end|>
@article{jin2024libriheavymix:,
    title={LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant
  Multi-Talker Speech Separation, ASR and Speaker Diarization},
    author={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},
    journal={arXiv preprint arXiv:2409.00819},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00819},
    primaryClass={cs.SD cs.CL eess.AS}
}