  Recently, deep neural network (DNN) based time-frequency (T-F) mask
estimation has shown remarkable effectiveness for speech enhancement.
Typically, a single T-F mask is first estimated based on DNN and then used to
mask the spectrogram of noisy speech in an order to suppress the noise. This
work proposes a multi-mask fusion method for speech enhancement. It
simultaneously estimates two complementary masks, e.g., ideal ratio mask (IRM)
and target binary mask (TBM), and then fuse them to obtain a refined mask for
speech enhancement. The advantage of the new method is twofold. First,
simultaneously estimating multiple complementary masks brings benefit endowed
by multi-target learning. Second, multi-mask fusion can exploit the
complementarity of multiple masks to boost the performance of speech
enhancement. Experimental results show that the proposed method can achieve
significant PESQ improvement and reduce the recognition error rate of back-end
over traditional masking-based methods. Code is available at
https://github.com/lc-zhou/mask-fusion.
