Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition
Table 2: The ASR WERs (%) for the DSN adapted acoustic models with respect to Nh reversal gradient coefficient α on the real development set of CHiME-3.
['[ITALIC] Nh', 'Reversal Gradient Coefficient  [ITALIC] α 1.0', 'Reversal Gradient Coefficient  [ITALIC] α 2.0', 'Reversal Gradient Coefficient  [ITALIC] α 3.0', 'Reversal Gradient Coefficient  [ITALIC] α 4.0', 'Reversal Gradient Coefficient  [ITALIC] α 5.0', 'Reversal Gradient Coefficient  [ITALIC] α 6.0', 'Reversal Gradient Coefficient  [ITALIC] α 7.0', 'Reversal Gradient Coefficient  [ITALIC] α 8.0', 'Reversal Gradient Coefficient  [ITALIC] α 9.0', 'Reversal Gradient Coefficient  [ITALIC] α Avg.']
We observe that the WER decreases with the growth of Nh, which is reasonable as the higher hidden representation of a well-trained DNN acoustic model is inherently more senone-discriminative and domain-invariant than the lower layers and can serve as a better initialization for the DSN unsupervised adaptation.