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arXiv · 2011.09081

Multi-Channel Automatic Speech Recognition Using Deep Complex Unet

Yuxiang Kong·Jian Wu·Quandong Wang·Peng Gao·Weiji Zhuang·Yujun Wang·Lei Xie
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Abstract

The front-end module in multi-channel automatic speech recognition (ASR) systems mainly use microphone array techniques to produce enhanced signals in noisy conditions with reverberation and echos. Recently, neural network (NN) based front-end has shown promising improvement over the conventional signal processing methods. In this paper, we propose to adopt the architecture of deep complex Unet (DCUnet) - a powerful complex-valued Unet-structured speech enhancement model - as the front-end of the multi-channel acoustic model, and integrate them in a multi-task learning (MTL) framework along with cascaded framework for comparison. Meanwhile, we investigate the proposed methods with several training strategies to improve the recognition accuracy on the 1000-hours real-world XiaoMi smart speaker data with echos. Experiments show that our proposed DCUnet-MTL method brings about 12.2% relative character error rate (CER) reduction compared with the traditional approach with array processing plus single-channel acoustic model. It also achieves superior performance than the recently proposed neural beamforming method.

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Yuxiang Kong, Jian Wu, Quandong Wang, Peng Gao, Weiji Zhuang, Yujun Wang, Lei Xie. 2020-11-18. Multi-Channel Automatic Speech Recognition Using Deep Complex Unet. https://arxiv.org/abs/2011.09081

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