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Extraction of novel features for emotion recognition

  • Information Technology
  • Published:
Journal of Shanghai University (English Edition)

Abstract

Hilbert-Huang transform method has been widely utilized from its inception because of the superiority in varieties of areas. The Hilbert spectrum thus obtained is able to reflect the distribution of the signal energy in a number of scales accurately. In this paper, a novel feature called ECC is proposed via feature extraction of the Hilbert energy spectrum which describes the distribution of the instantaneous energy. The experimental results conspicuously demonstrate that ECC outperforms the traditional short-term average energy. Combination of the ECC with mel frequency cepstral coefficients (MFCC) delineates the distribution of energy in the time domain and frequency domain, and the features of this group achieve a better recognition effect compared with the feature combination of the short-term average energy, pitch and MFCC. Afterwards, further improvements of ECC are developed. TECC is gained by combining ECC with the teager energy operator, and EFCC is obtained by introducing the instantaneous frequency to the energy. In the experiments, seven status of emotion are selected to be recognized and the highest recognition rate 83.57% is achieved within the classification accuracy of boredom reaching 100%. The numerical results indicate that the proposed features ECC, TECC and EFCC can improve the performance of speech emotion recognition substantially.

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Authors and Affiliations

  1. School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200072, P. R. China

    Xiang Li  (李 翔) & Xin Li  (李 昕)

  2. School of Computer Engineering and Science, Shanghai University, Shanghai, 200072, P. R. China

    Yu Zheng  (郑 宇)

Authors
  1. Xiang Li  (李 翔)
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  2. Yu Zheng  (郑 宇)
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  3. Xin Li  (李 昕)
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Corresponding author

Correspondence to Xin Li  (李 昕).

Additional information

Project supported by the State Key Laboratory of Robotics and System (Grant No.SKLS-2009-MS-10), and the Shanghai Leading Academic Discipline Project (Grant No.J50103)

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Li, X., Zheng, Y. & Li, X. Extraction of novel features for emotion recognition. J. Shanghai Univ.(Engl. Ed.) 15, 479–486 (2011). https://doi.org/10.1007/s11741-011-0772-3

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  • DOI: https://doi.org/10.1007/s11741-011-0772-3

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