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arXiv · 2405.11078

Acoustic modeling for Overlapping Speech Recognition: JHU Chime-5 Challenge System

Vimal Manohar·Szu-Jui Chen·Zhiqi Wang·Yusuke Fujita·Shinji Watanabe·Sanjeev Khudanpur
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Abstract

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.

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Vimal Manohar, Szu-Jui Chen, Zhiqi Wang, Yusuke Fujita, Shinji Watanabe, Sanjeev Khudanpur. 2024-05-17. Acoustic modeling for Overlapping Speech Recognition: JHU Chime-5 Challenge System. https://doi.org/0.1109/icassp.2019.8682556

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