计算机工程与应用2016，Vol.52Issue(21)：157-161,5.DOI:10.3778/j.issn.1002-8331.1601-0298
基于深度信念网络的语音服务文本分类
Voice service text classification based on deep belief network. Com-puter Engineering and Applications, 2016, 52(21):157-161
摘要
Abstract
Online artificial voice service has been expanded in the business activities. In order to provide better customer service, it is needed to do effective evaluation of the quality of voice service. The purpose is to change artificial voice ser-vices into text using voice recognition technology and then classify. Common text classification models are Naive Bayes, KNN, back propagation neural networks, support vector machines and other models that are more dependent on the char-acteristics of the speech text representation after pretreatment and prone to the curse of dimensionality, local optimization and long training time. The Deep Belief Network model(DBN)can learn from the characteristics expressed in the text preprocessed to feature a more essential representation which eases classifiers and avoids the problems of above models. After text of the artificial voice service, through the deep belief network model conversion feature representation and then classification, the final classification results than the direct use of text features classification model have slightly increased.关键词
特征/分类/语音/深度信念网络模型(DBN)/受限玻尔兹曼机(RBM)Key words
feature/classification/voice/Deep Belief Network model(DBN)/Restricted Boltzmann Machine(RBM)分类
信息技术与安全科学引用本文复制引用
周世超,张沪寅,杨冰..基于深度信念网络的语音服务文本分类[J].计算机工程与应用,2016,52(21):157-161,5.基金项目
高等学校博士学科点专项科研基金 （）
武汉市科学技术局项目（No.201302038）。 （）
