  This paper describes a practical dual-process speech enhancement system that
adapts environment-sensitive frame-online beamforming (front-end) with help
from environment-free block-online source separation (back-end). To use minimum
variance distortionless response (MVDR) beamforming, one may train a deep
neural network (DNN) that estimates time-frequency masks used for computing the
covariance matrices of sources (speech and noise). Backpropagation-based
run-time adaptation of the DNN was proposed for dealing with the mismatched
training-test conditions. Instead, one may try to directly estimate the source
covariance matrices with a state-of-the-art blind source separation method
called fast multichannel non-negative matrix factorization (FastMNMF). In
practice, however, neither the DNN nor the FastMNMF can be updated in a
frame-online manner due to its computationally-expensive iterative nature. Our
DNN-free system leverages the posteriors of the latest source spectrograms
given by block-online FastMNMF to derive the current source covariance matrices
for frame-online beamforming. The evaluation shows that our frame-online system
can quickly respond to scene changes caused by interfering speaker movements
and outperformed an existing block-online system with DNN-based beamforming by
5.0 points in terms of the word error rate.
