Experimental Research on Mongolian Acoustic Model Structure Based on DNN-HMM

As a hybrid modeling technology in speech recognition, DNN-HMM is composed of deep neural networks and hidden Markov models. In the process of using the Mongolian corpus to construct the DNN-HMM acoustic model, in order to study the influence of the DNN-HMM structure on the Mongolian acoustic modeling and the relationship between the size of the Mongolian corpus and the DNN-HMM acoustic model structure, the DNN-HMM acoustic model was designed For the structure of DNN in the model, four DNN-HMM acoustic models of Rectangle DNN-HMM, Trapezoid DNN-HMM, Polygon DNN-HMM and Hourglass DNN-HMM are proposed. Experiments are carried out on the basis of the Kaldi experimental platform, phonemes are selected as the modeling unit, and three-scale Mongolian corpora are used to construct four-structure DNN-HMM acoustic models. The experimental results of the depth structure and the width structure show that the Polygon DNN - HMM structure with a depth of 6 layers is suitable for Mongolian acoustic model modeling. As the corpus increases, the width of the acoustic model should be appropriately increased so that each layer of the acoustic model can be Learn more abundant features and improve the accuracy of speech recognition.