  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.
