LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910586316521472 |
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| author | Jin, Zengrui Yang, Yifan Shi, Mohan Kang, Wei Yang, Xiaoyu Yao, Zengwei Kuang, Fangjun Guo, Liyong Meng, Lingwei Lin, Long Xu, Yong Zhang, Shi-Xiong Povey, Daniel |
| author_facet | Jin, Zengrui Yang, Yifan Shi, Mohan Kang, Wei Yang, Xiaoyu Yao, Zengwei Kuang, Fangjun Guo, Liyong Meng, Lingwei Lin, Long Xu, Yong Zhang, Shi-Xiong Povey, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00819 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization Jin, Zengrui Yang, Yifan Shi, Mohan Kang, Wei Yang, Xiaoyu Yao, Zengwei Kuang, Fangjun Guo, Liyong Meng, Lingwei Lin, Long Xu, Yong Zhang, Shi-Xiong Povey, Daniel Sound Computation and Language Audio and Speech Processing 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. |
| title | LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.00819 |