  The performance of speech enhancement and separation systems in anechoic
environments has been significantly advanced with the recent progress in
end-to-end neural network architectures. However, the performance of such
systems in reverberant environments is yet to be explored. A core problem in
reverberant speech separation is about the training and evaluation metrics.
Standard time-domain metrics may introduce unexpected distortions during
training and fail to properly evaluate the separation performance due to the
presence of the reverberations. In this paper, we first introduce the
"equal-valued contour" problem in reverberant separation where multiple outputs
can lead to the same performance measured by the common metrics. We then
investigate how "better" outputs with lower target-specific distortions can be
selected by auxiliary autoencoding training (A2T). A2T assumes that the
separation is done by a linear operation on the mixture signal, and it adds an
loss term on the autoencoding of the direct-path target signals to ensure that
the distortion introduced on the direct-path signals is controlled during
separation. Evaluations on separation signal quality and speech recognition
accuracy show that A2T is able to control the distortion on the direct-path
signals and improve the recognition accuracy.
