UNSUPERVISED SPEAKER ADAPTATION OF BATCH NORMALIZED ACOUSTIC MODELS FOR ROBUST ASR

Batch normalization is a standard technique for training deep neural networks. In batch normalization, the input of each hidden layer is first mean-variance normalized and then linearly transformed before applying non-linear activation functions. We propose a novel unsupervised speaker adaptation technique for batch normalized acoustic models. The key idea is to adjust the linear transformations previously learned by batch normalization for all the hidden layers according to the first-pass decoding results of the speaker-independent model. With the adjusted linear transformations for each test speaker, the test distribution of the input of each hidden layer better matches the training distribution. Experiments on the CHiME-3 dataset demonstrate the effectiveness of the proposed layer-wise adaptation approach. Our overall system obtains 4.24% WER on the real subset of the test data, which represents the best reported result on this dataset to date and a relative 27.3% error reduction over the previous best result.