Comparison of Regularization Constraints in Deep Neural Network based Speaker Adaptation

Adaptation of deep neural network (DNN) acoustic model has been proven to significantly improve the automatic speech recognition (ASR) performance. But how to improve the generalization ability of the adapted model is still a challenge problem. In this study, we investigated algorithms to improve model generalization ability in a parameter regularization framework. Although some regularization algorithms have been proposed, there is no investigation on how the effects of using different regularization constraints in adaptation (e.g., parameter space smoothness or sparsity etc.). We investigated regularization constraints in a lp regularization framework which includes l(1), l(2) regularization, and several constrained forms of them. We carried out the investigation on a lecture speech recognition task. Our investigation showed that most of the regularization constraints could improve the performance but with different parameter updating mechanisms. The regularization constraint which makes the adaptation to pick up only a few model parameters for updating showed the most effective. In addition, by combining different regularization constraints, further improvements could be achieved.