Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition
Table 4: Results of adaptation on Italian data with f=2, λ=2.0.
['Hours', 'WER (%)', 'RERR (%)']
Next we use the best combination of the gradient reversal coefficient λ and the feature layer index f from the previous experiment to evaluate contribution of various amounts of adaptation data to accuracy of adapted model. Results are listed in Tab. What emerges from the results reported here is that no significant drop in WER is observed if amount of adaptation data is decreased to 30 hours or one third of originally evaluated adaptation data set. On the one hand this finding suggests that one does not need to acquire large amount of the target domain data in order to get a moderate improvement of ASR system trained on clean speech data. On the other hand, it is possible that this effect of 30 hours of adaptation data is due to a good chance of having comparable number of distinct recording conditions in the adaptation and testing data and may not be generalizable to a larger testing data set with more diverse set of recording conditions.