An unsupervised deep domain adaptation approach for robust speech recognition

This paper addresses the robust speech recognition problem as a domain adaptation task. Specifically, we introduce an unsupervised deep domain adaptation (DDA) approach to acoustic modeling in order to eliminate the training–testing mismatch that is common in real-world use of speech recognition. Under a multi-task learning framework, the approach jointly learns two discriminative classifiers using one deep neural network (DNN). As the main task, a label predictor predicts phoneme labels and is used during training and at test time. As the second task, a domain classifier discriminates between the source and the target domains during training. The network is optimized by minimizing the loss of the label classifier and to maximize the loss of the domain classifier at the same time. The proposed approach is easy to implement by modifying a common feed-forward network. Moreover, this unsupervised approach only needs labeled training data from the source domain and some unlabeled raw data of the new domain. Speech recognition experiments on noise/channel distortion and domain shift confirm the effectiveness of the proposed approach. For instance, on the Aurora-4 corpus, compared with the acoustic model trained only using clean data, the DDA approach achieves relative 37.8% word error rate (WER) reduction.