  Due to the unprecedented breakthroughs brought about by deep learning, speech
enhancement (SE) techniques have been developed rapidly and play an important
role prior to acoustic modeling to mitigate noise effects on speech. To
increase the perceptual quality of speech, current state-of-the-art in the SE
field adopts adversarial training by connecting an objective metric to the
discriminator. However, there is no guarantee that optimizing the perceptual
quality of speech will necessarily lead to improved automatic speech
recognition (ASR) performance. In this study, we present TENET, a novel
Time-reversal Enhancement NETwork, which leverages the transformation of an
input noisy signal itself, i.e., the time-reversed version, in conjunction with
the siamese network and complex dual-path transformer to promote SE performance
for noise-robust ASR. Extensive experiments conducted on the Voicebank-DEMAND
dataset show that TENET can achieve state-of-the-art results compared to a few
top-of-the-line methods in terms of both SE and ASR evaluation metrics. To
demonstrate the model generalization ability, we further evaluate TENET on the
test set of scenarios contaminated with unseen noise, and the results also
confirm the superiority of this promising method.
