Speech Emotion Recognition Using Regularized Discriminant Analysis

  • Swarna Kuchibhotla
  • B. S. Yalamanchili
  • H. D. Vankayalapati
  • K. R. Anne
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 247)


Speech emotion recognition plays a vital role in the field of Human Computer Interaction. The aim of speech emotion recognition system is to extract the information from the speech signal and identify the emotional state of a human being. The information extracted from the speech signal is to be appropriate for the analysis of the emotions. This paper analyses the characteristics of prosodic and spectral features. In addition feature fusion technique is also used to improve the performance. We used Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM), K-Nearest Neighbor (KNN) as a Classifiers. Results suggest that spectral features outperform prosodic features. Results are validated over Berlin and Spanish emotional speech databases.


Mel Frequency Cepstral Coefficients (MFCC) Pitch Energy Feature Fusion 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ververidis, D., Kotropoulos, C.: Emotional speech recognition: Resources, features, and methods. Speech Communication 48, 1162–1181 (2006)CrossRefGoogle Scholar
  2. 2.
    Ayadi, M.E., Kamel, M.S., Karray, F.: Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition 44, 572–587 (2011)CrossRefMATHGoogle Scholar
  3. 3.
    Luengo, I., Navas, E., Hernáez, I.: Feature Analysis and Evoluation for Automatic Emotion Identification in Speech. IEEE Transctions on Multimedia 12(6) (October 2010)Google Scholar
  4. 4.
    Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.G.: Emotion Recognition in Human Computer Interaction. IEEE Signal Processing Magazine (January 2001)Google Scholar
  5. 5.
    Vogt, T., André, E., Wagner, J.: Automatic recognition of emotions from speech: A review of the literature and recommendations for practical realisation. In: Peter, C., Beale, R. (eds.) Affect and Emotion in Human-Computer Interaction. LNCS, vol. 4868, pp. 75–91. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Muda, L., Begam, M., Elamvazuthi, I.: Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques. Journal of Computing 2(3) (March 2010) ISSN 2151-9617Google Scholar
  7. 7.
    Ji, S., Ye, J.: Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection. IEEE Transactions on Neural Networks 19(10) (October 2008)Google Scholar
  8. 8.
    Ye, J., Xiong, T., Janardan, R., Bi, J., Cherkassky, V., Kambhamettu, C.: Efficient model selection for regularized linear discriminant analysis. In: Proc. CIKM Arlington, VA, pp. 532–539 (2006)Google Scholar
  9. 9.
    Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)MATHGoogle Scholar
  10. 10.
    Suresh, M., Ravikumar, M.: Dimensionality Reduction and Classification of Color Features data using SVM and KNN. International Journal of Image Processing and Visual Communication 1(4), 2319–1724 (February 2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Swarna Kuchibhotla
    • 1
  • B. S. Yalamanchili
    • 2
  • H. D. Vankayalapati
    • 3
  • K. R. Anne
    • 2
  1. 1.Acharya Nagarjuna UniversityGunturIndia
  2. 2.Department of Information TechnologyVRSECVijayawadaIndia
  3. 3.Department of Computer Science and EngineeringVRSECVijayawadaIndia

Personalised recommendations