Distance metric learning for kernel density-based acoustic model under limited training data conditions

Kernel density model works well for limited training data in acoustic modeling. In this paper, we improve the kernel density-based acoustic model for low resource language speech recognition. In our previous study, we demonstrated the effectiveness of the kernel density-based acoustic model on discriminative features such as cross-lingual bottleneck features. In this paper, we propose to learn a Mahalanobis-based distance, which is equivalent to a full rank linear feature transformation, to minimize training data frame classification error. Experimental results on the Wall Street Journal (WSJ) task show that the proposed Mahalanobis-based distance learning results in significant improvements over the Euclidean distance. The kernel density acoustic model with the Mahalanobis-based distance also outperforms deep neural network acoustic model significantly in limited training data cases.