Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition
Table 5: Results of adaptation on French data with f=2, λ=2.0.
['Hours', 'WER (%)', 'RERR (%)']
We also run experiments on the same amounts of French data to see if it is important to use adaptation data for the same language as the language of interest. Results are listed in Tab. Interestingly, the method improves WER even when used with the adaptation data for French while the language of interest is Italian. We also observe the same trend regarding amount of French adaptation data as with Italian adaptation data, namely insignificant contribution of additional adaptation data, besides 30 hours, to WER. Hence, it could conceivably be hypothesized that the method makes DNN more robust to a number of different recording conditions in general and not only to the recording conditions represented in the adaptation data.