Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition
Table 3: Results (WER in %) of adaptation on 125 hours of Channel 4.
['[ITALIC] f', '[ITALIC] λ', '[ITALIC] λ', '[ITALIC] λ']
First we use the fixed target domain data subset, namely 125 hours of Channel 4 recordings, to evaluate the effect of various combinations of the gradient reversal coefficient λ and the feature layer index f. WER and relative error rate reduction (RERR) are listed in Tab. The best combination is gradient reversal coefficient λ=2.0 and feature layer index f=2 and it results WER of 68.3%, which is within almost twice smaller gap with the best result of 46.0%, obtained by supervised training on Channels 1–4, compared to 85.2% resulted by unadapted model trained on Channel 1. In addition to that, we repeat the same experiment with Channels 2–4 as adaptation data, as the best baseline system also utilizes this data. We obtain WER of 66.6% and conclude that the gain in WER is too small in comparison to amount of additional adaptation data.