Spectral feature mapping with mimic loss for robust speech recognition
Table 1: Experimental results on the CHiME2 test set; CE WER is the word error rate of a DNN-HMM hybrid system trained using a cross-entropy criterion. sMBR WER is the error rate after sequential minimum Bayes risk training.
['Spectral input to Kaldi', 'CE WER', 'sMBR WER']
The difference in performance between pre- and post-softmax targets may be due to a mismatch between target domain and loss criterion; ongoing work suggests that a cross-entropy mimic loss on post-softmax targets performs similarly to MSE on pre-softmax. Furthermore, the information loss in the softmax normalization may “broaden” the allowable spectral mappings, harming generalization. This suggests that it is helpful for the noisy recognizer not only to have the same targets as the clean recognizer, but also to learn to behave in the same way as the clean recognizer.