Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition
Table 2: Results of the baseline model.
['System', 'Training data', 'WER (%)']
Results of the baseline model trained on different data sets with different labels and tested on 15 hours of Italian SPEECON channel 4 are shown in Tab. It is apparent from this table that decoding of noisy reverberated speech is a challenging task. While WER of the model trained on Channel 1 is incredibly high at 85.2% due to significant distortions introduced to speech by environmental noises and reverberations in the testing noisy speech data, the model trained on Channel 4 achieves significantly lower WER by learning to normalize these distortions from the training data, and the model trained on Channels 1–4 results even better WER because of the generalized representations of clean and noisy data samples presenting in the training data. Our analysis of problematic utterances suggests that as the majority of the mistakes are made in ,,Spontaneous speech”, ,,Numbers, times, dates” and ,,Named entities” categories, where the language model could not be helpful. An alternative to the proposed method would be to train a model on the target domain data and the labels produced by a first pass of unadapted model. As it follows from Tab. Moreover, the proposed method has an advantage of applicability in a crosslingual setup.