A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM | Zendy

Chenchen Huang | Zendy; Wei Gong | Zendy; Wenlong Fu | Zendy; Dongyu Feng | Zendy

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A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

Author(s) -

Chenchen Huang,

Wei Gong,

Wenlong Fu,

Dongyu Feng

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/749604

Subject(s) - speech recognition , deep belief network , emotion recognition , computer science , feature extraction , support vector machine , classifier (uml) , artificial intelligence , pattern recognition (psychology) , mel frequency cepstrum , speaker recognition , feature (linguistics) , artificial neural network , linguistics , philosophy

Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method

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