  Speech dereverberation is an important issue for many real-world speech
processing applications. Among the techniques developed, the weighted
prediction error (WPE) algorithm has been widely adopted and advanced over the
last decade, which blindly cancels out the late reverberation component from
the reverberant mixture of microphone signals. In this study, we extend the
neural-network-based virtual acoustic channel expansion (VACE) framework for
the WPE-based speech dereverberation, a variant of the WPE that we recently
proposed to enable the use of dual-channel WPE algorithm in a single-microphone
speech dereverberation scenario. Based on the previous study, some ablation
studies are conducted regarding the constituents of the VACE-WPE in an offline
processing scenario. These studies help understand the dynamics of the system,
thereby simplifying the architecture and leading to the introduction of new
strategies for training the neural network for the VACE. Experimental results
in noisy reverberant environments reveal that VACE-WPE considerably outperforms
its single-channel counterpart in terms of objective speech quality and is
complementary to the single-channel WPE when employed as the front-end for the
far-field automatic speech recognizer.
