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《Journal of Data Acquisition and Processing》 2011-01
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Speech Emotion Recognition Based on Particle Swarm Optimizer Neural Network

Yu Hua1,2,Huang Chengwei2,Jin Yun2,Zhao Li2(1.Electronic Information College,Nanjing Information Occupation Technical College,Nanjing,210013,China;2.School of Information Science and Engineering,Southeast University,Nanjing,210096,China)  
Particle swarm optimizer(PSO) neural network is used in speech emotion recognition system.According to the dimensional model,emotion features are extracted and categorized into prosody features and voice quality features.HNR feature is studied corresponding to different emotion categories.The particle swarm optimizer is used to train the random initial data to optimize the connection weights and thresholds of the neural network,thus having a fast network convergence speed.In the recognition experiments,BP neural network,RBF neural network and PSO neural network are compared in the same testing environment.The recognitino rate of PSO neural network are 6.7% more than BP neural network and 5.4% more than RBF neural network.Results show that particle swarm optimizer neural network brings a promising improvement in the classification ability for speech emotion recognition.
【Fund】: 国家自然科学基金(60472058 60975017)资助项目;; 江苏省自然科学基金(BK200829)资助项目
【CateGory Index】: TN912.34
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