Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation
Table 1: The WER (%) performance of unadapted, T/S learning adapted LSTM acoustic models for robust ASR on the real noisy channel 5 test set of CHiME-3.
['System', 'Adaptation Data', 'BUS', 'CAF', 'PED', 'STR', 'Avg.']
The unadapted Cortana model has 6.96% WER on the clean test set. After T/S learning with both the “clean-noisy” and “clean-clean” parallel data, the student model has 6.99% WER on the clean test. As the focus of this study is to improve T/S adaptation on noisy test data, we will only report results with the CHiME-3 real noisy channel 5 test set. The T/S learning achieves 13.88% and 13.56% average WERs when adapted to “clean-noisy” and “clean-noisy & clean-clean” respectively, which are 40.05% and 41.45% relative improvements over the unadapted clean model. Note that our experimental setup does not achieve the state-of-the-art performance on CHiME-3 dataset (e.g., we did not perform beamforming, sequence training or use RNN LM for decoding.) since our goal is to simply verify the effectiveness of adversarial T/S learning in achieving condition-robust unsupervised adaptation.