A Parameter Transfer Method for HMM-DNN Heterogeneous Model with the Scarce Mongolian Data Set

Abstract

Hidden Markov Model-Deep Neural Network (HMM-DNN) is one of the most successful architecture in speech recognition. Although the HMM-DNN achieved state-of-the-art results on English and Mandarin, we find that there are lots of not updated parameters during the training of HMM-DNN acoustic model on a small scale Mongolian data set. This caused the model’s network training underfitting, which cannot learn the features of the data set. In the speech recognition scenario, the underfitting of speech features leads to a problem that the accuracy of the system decreases. In this regard, we define the concept of the homogeneous model heterogeneous model and propose a parameter learning method for HMM-DNN heterogeneous model in the scarce Mongolian data set. In our experiment, we choose KALDI as the experimental platform with the TIMIT English data set as the source data set and the scarce Mongolian data set as the target data set. Through the proposed parameter transfer method, we achieved much better performance on Mongolian recognition accuracy.