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Erschienen in:
2005 | OriginalPaper | Buchkapitel
EmoEars: An Emotion Recognition System for Mandarin Speech
verfasst von : Bo Xie, Ling Chen, Gen-Cai Chen, Chun Chen
Erschienen in: Computational Intelligence and Security
Verlag: Springer Berlin Heidelberg
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In this paper, an emotion recognition system for mandarin speech is presented. Five basic human emotions including angry, fear, happy, neutral and sad are investigated. The recognizer is based on neural network with OCON and ACON architecture. Some novel feature selection methods are also added as optional tool to enhance the efficiency and classification accuracy. The system can train speaker dependent emotion speech model through online emotional utterance recording. Experiment results show that emotion can be recognized through neural network model, the best mean accuracy is 86.7%. In addition, the feature selection module is effective to reduce the compute load and increase the generalization ability of the recognizer.