Speech Emotion Recognition Based on Principal Component Analysis and Back Propagation Neural Network

摘要：

Speech signal carries rich emotional information except semantic information. Five common emotions, namely happiness, anger, boredom, fear and sadness, were discussed and recognized through a proposed framework which combines Principal Component Analysis and Back Propagation neutral network. The candidate parameters were refined from 43 to 11 via PCA to stand for a certain emotional type. Two neural network models, One Class One Network and All Class One Network, were employed and compared.The promising result, ranging from 52％-62％, suggests that the framework is feasible to be used for recognizing emotions in spoken utterance.

关键词： BP Neutral Network PCA Emotion Controller

作者: Sheguo Wang Xuxiong Ling Fuliang Zhang Jianing Tong

作者单位: Information and Electronics Engineering Institute HeBei University Of Engineering Handan, China

会议类型: 国际会议

会议名称: 2010 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2010)(2010年检测技术与机电自动化国际会议)

会议地点: 长沙

会议语种:英文

页码: 2691-2694

在线出版日期: 2010-03-13（万方平台首次上网日期，不代表论文的发表时间）