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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.11078 |
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| _version_ | 1866909206780575744 |
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| author | Manohar, Vimal Chen, Szu-Jui Wang, Zhiqi Fujita, Yusuke Watanabe, Shinji Khudanpur, Sanjeev |
| author_facet | Manohar, Vimal Chen, Szu-Jui Wang, Zhiqi Fujita, Yusuke Watanabe, Shinji Khudanpur, Sanjeev |
| contents | This paper summarizes our acoustic modeling efforts in the Johns Hopkins University speech recognition system for the CHiME-5 challenge to recognize highly-overlapped dinner party speech recorded by multiple microphone arrays. We explore data augmentation approaches, neural network architectures, front-end speech dereverberation, beamforming and robust i-vector extraction with comparisons of our in-house implementations and publicly available tools. We finally achieved a word error rate of 69.4% on the development set, which is a 11.7% absolute improvement over the previous baseline of 81.1%, and release this improved baseline with refined techniques/tools as an advanced CHiME-5 recipe. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11078 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Acoustic modeling for Overlapping Speech Recognition: JHU Chime-5 Challenge System Manohar, Vimal Chen, Szu-Jui Wang, Zhiqi Fujita, Yusuke Watanabe, Shinji Khudanpur, Sanjeev Audio and Speech Processing This paper summarizes our acoustic modeling efforts in the Johns Hopkins University speech recognition system for the CHiME-5 challenge to recognize highly-overlapped dinner party speech recorded by multiple microphone arrays. We explore data augmentation approaches, neural network architectures, front-end speech dereverberation, beamforming and robust i-vector extraction with comparisons of our in-house implementations and publicly available tools. We finally achieved a word error rate of 69.4% on the development set, which is a 11.7% absolute improvement over the previous baseline of 81.1%, and release this improved baseline with refined techniques/tools as an advanced CHiME-5 recipe. |
| title | Acoustic modeling for Overlapping Speech Recognition: JHU Chime-5 Challenge System |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2405.11078 |