  Many speech enhancement methods try to learn the relationship between noisy
and clean speech, obtained using an acoustic room simulator. We point out
several limitations of enhancement methods relying on clean speech targets; the
goal of this work is proposing an alternative learning algorithm, called
acoustic and adversarial supervision (AAS). AAS makes the enhanced output both
maximizing the likelihood of transcription on the pre-trained acoustic model
and having general characteristics of clean speech, which improve
generalization on unseen noisy speeches. We employ the connectionist temporal
classification and the unpaired conditional boundary equilibrium generative
adversarial network as the loss function of AAS. AAS is tested on two datasets
including additive noise without and with reverberation, Librispeech + DEMAND
and CHiME-4. By visualizing the enhanced speech with different loss
combinations, we demonstrate the role of each supervision. AAS achieves a lower
word error rate than other state-of-the-art methods using the clean speech
target in both datasets.
