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

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4 Author(s)
Sheguo Wang ; Inf. & Electron. Eng. Inst., HeBei Univ. Of Eng., Handan, China ; Xuxiong Ling ; Fuliang Zhang ; Jianing Tong

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.

Published in:

Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on  (Volume:3 )

Date of Conference:

13-14 March 2010