  This paper describes multichannel speech enhancement for improving automatic
speech recognition (ASR) in noisy environments. Recently, the minimum variance
distortionless response (MVDR) beamforming has widely been used because it
works well if the steering vector of speech and the spatial covariance matrix
(SCM) of noise are given. To estimating such spatial information, conventional
studies take a supervised approach that classifies each time-frequency (TF) bin
into noise or speech by training a deep neural network (DNN). The performance
of ASR, however, is degraded in an unknown noisy environment. To solve this
problem, we take an unsupervised approach that decomposes each TF bin into the
sum of speech and noise by using multichannel nonnegative matrix factorization
(MNMF). This enables us to accurately estimate the SCMs of speech and noise not
from observed noisy mixtures but from separated speech and noise components. In
this paper we propose online MVDR beamforming by effectively initializing and
incrementally updating the parameters of MNMF. Another main contribution is to
comprehensively investigate the performances of ASR obtained by various types
of spatial filters, i.e., time-invariant and variant versions of MVDR
beamformers and those of rank-1 and full-rank multichannel Wiener filters, in
combination with MNMF. The experimental results showed that the proposed method
outperformed the state-of-the-art DNN-based beamforming method in unknown
environments that did not match training data.
