Unpaired Speech Enhancement by Acoustic and Adversarial Supervision for Speech Recognition
TABLE II: WERs (%) and DCE of different speech enhancement methods on CHiME4-simulated test set
['Method', 'WER (%)', 'DCE']
Table [CONTINUE] and II show the WER and DCE (normalized by the number of frames) on the test set of Librispeech + DEMAND, and CHiME-4. The Wiener filtering method shows lower DCE, but higher WER than no enhancement. We conjecture that Wiener filter remove some fraction of noise, however, remaining speech is distorted as well. The adversarial supervision (i.e., wAC = 0, wAD > 0) consistently shows very high WER (i.e., > 90%), because the enhanced sample tends to have less correlation with noisy speech, as shown in Fig. 3. [CONTINUE] The same tendency is observed in CHiME-4 (i.e. acoustic supervision (27.7%) and multi-task learning (26.1%) show lower WER than minimizing DCE (31.1%) and FSEGAN (29.1%)).