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

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  • Chenchen Huang
  • Wei Gong
  • Wenlong Fu
  • Dongyu Feng

Abstract

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.

Suggested Citation

  • Chenchen Huang & Wei Gong & Wenlong Fu & Dongyu Feng, 2014. "A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-7, August.
    • Handle: RePEc:hin:jnlmpe:749604
    DOI: 10.1155/2014/749604
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    Cited by:

    1. Chen, Yuan & Han, Dongmei & Zhou, Xiaofeng, 2023. "Mining the emotional information in the audio of earnings conference calls : A deep learning approach for sentiment analysis of securities analysts' follow-up behavior," International Review of Financial Analysis, Elsevier, vol. 88(C).

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