智能系统学报2018，Vol.13Issue(3)：486-492,7.DOI:10.11992/tis.201710029
基于深度神经网络的蒙古语声学模型建模研究
Mongolian acoustic modeling based on deep neural network
摘要
Abstract
Considering the difficulty of using the Gaussian mixture model (GMM) to adequately describe the correlation and independence hypothesis of the Mongolian acoustic features in the acoustic modeling of Mongolian speech recognition,this study investigates an acoustic model based on deep neural network (DNN).Firstly,using DNN,the internal structure of phonetic features were classified and learned to extract the Mongolian acoustic features,and a DNNHMM Mongolian acoustic model was constructed.Secondly,a training algorithm was designed by combining unsupervised pre-training and supervised training tuning.In addition,dropout technology was added into the DNN-HMM Mongolian acoustic model training to avoid the over-fitting phenomenon.Finally,a comparative experiment was conducted for the GMM-HMM and DNN-HMM Mongolian acoustic models on basis of the small-scale corpus and Kaldi experimental platform.Experimental results show that the word recognition error rate of DNN-HMM Mongolian model was reduced by 7.5％ and sentence recognition error rate was reduced by 13.63％.In addition,the over-fitting of DNN-HMM Mongolian acoustic model can be effectively avoided by adopting the dropout technique during training.关键词
语音识别/声学模型/GMM-HMM/DNN-HMM/监督学习/预训练/过拟合/dropoutKey words
speech recognition/acoustic model/GMM-HMM/DNN-HMM/supervised learning/pre-training/over-fitting/dropout分类
信息技术与安全科学引用本文复制引用
马志强,李图雅,杨双涛,张力..基于深度神经网络的蒙古语声学模型建模研究[J].智能系统学报,2018,13(3):486-492,7.基金项目
国家自然科学基金项目(61762070,61650205). （61762070,61650205）
