Refinements of regression-based context-dependent modelling of deep neural networks for automatic speech recognition
Guangsen Wang, Khe Chai Sim · 2014
The data sparsity problem of context-dependent (CD) acoustic modelling of deep neural networks (DNNs) in speech recognition is addressed by using the decision tree state clusters as the training targets. The CD states within a cluster cannot be distinguished during decoding. This problem, referred to as the clustering problem, is not explicitly addressed in the current literature. In our previous work, a regression-based CD-DNN framework was proposed to address both the data sparsity and the clustering problems. This paper investigates several refinements for the regression-based CD-DNN including two more representative state approximation schemes and the incorporation of sequential learning. The two approximations are obtained based on the statistics learned from the training data. Sequential learning is applied to both broad phone DNN detectors and the regression NN. The proposed refinements are evaluated on a broadcast news transcription task. For the cross-entropy systems, the two approximations perform consistently better than our previous work. Consistent performance gain over the corresponding cross-entropy trained systems is also observed for both the baseline CD-DNN and the regression model with sequential learning.