The elastic spatial filter (ESF) proposed in recent years is a popular
multi-channel speech enhancement front end based on deep
neural network (DNN). It is suitable for real-time processing
and has shown promising automatic speech recognition (ASR)
results. However, the ESF only utilizes the knowledge of fixed
beamforming, resulting in limited noise reduction capabilities.
In this paper, we propose a DNN-based generalized sidelobe
canceller (GSC) that can automatically track the target speaker’s
direction in real time and use the blocking technique to generate
reference noise signals to further reduce noise from the
fixed beam pointing to the target direction. The coefficients in
the proposed GSC are fully learnable and an ASR criterion is
used to optimize the entire network. The 4-channel experiments
show that the proposed GSC achieves a relative word error rate
improvement of 27.0% compared to the raw observation, 20.6%
compared to the oracle direction-based traditional GSC, 10.5%
compared to the ESF and 7.9% compared to the oracle maskbased
generalized eigenvalue (GEV) beamformer.