A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation

摘要

In this paper we investigate the GMM-derived features for adaptation of context-dependent deep neural network HMM (CD-DNN-HMM) acoustic models with the focus on exploration of fusion of the adapted GMM-derived features and the conventional bottleneck features. We analyze and compare different types of fusion, such as feature level, posterior level, lattice level and others in order to discover the best possible way of fusion. Experimental results on the TED-LIUM corpus show that the proposed adaptation technique can be effectively integrated into DNN setup at different levels and provide additional gain in recognition performance: up to 6% of relative word error rate reduction (WERR) over the strong speaker adapted DNN baseline, and up to 22% of relative WERR in comparison with a speaker independent DNN baseline model, trained on conventional features.