Text-Dependent Versus Text-Independent Speech Emotion Recognition

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)


The communication between individual and equipment is through speech emotion recognition which plays a vital role and is very exigent to handle. Today, this filed has become an important area of research. It has wide range of applications. This paper analyzes the performance of emotion recognition for eight speakers. Indian Institute of Technology Kharagpur Simulated Hindi Emotional Speech Corpus (IITKGP-SEHSC) emotional speech corpora used for emotions recognition. The sentiments under surveillance for this study are anger, fear, happy, neutral, sarcastic, and surprise. The categorization is prepared using Gaussian mixture model (GMM). Mel-frequency cepstral coefficients (MFCCs) attributes have been used for defining the emotions. We have extracted the percentage of accuracy of emotion for both text-dependent data and text-independent data. We also observed that emotion recognition performance depends on text and speaker. We found that the percentage of accuracy of text-dependent data is more than the text-independent data.


Speech emotion recognition Gaussian mixture model Male-scale frequency cepstral coefficient IITKGP-SEHSC 


  1. 1.
    Koolagudi, S., Rao, K.S.: Emotion recognition from speech using source, system, and prosodic features. Int. J. Speech Technol. 15, 265–289 (2012)CrossRefGoogle Scholar
  2. 2.
    Ververidis, D., Kotropoulos, C.: Emotional speech recognition: resources, features, and methods. SPC 48, 1162–1181 (2006)Google Scholar
  3. 3.
    Rabiner, L.R., Juang, B.H.: Fundamentals of Speech Recognition. Prentice-Hall, Englewood Cliffs (1993)Google Scholar
  4. 4.
    Koolagudi, S.G., Maity, S., Kumar, V.A., Chakrabarti, S., Rao, K.S.: IITKGP-SESC: Speech Database for Emotion Analysis, vol. 40, pp. 485–492. Springer, Berlin (2009)Google Scholar
  5. 5.
    Koolagudi, S., Reddy, R., Yadav, J., Rao, K.S.: IITKGP-SEHSC: Hindi speech corpus for emotion analysis. In International Conference on Devices and Communications (ICDeCom), pp. 1–5 (2011)Google Scholar
  6. 6.
    Moataz, M.H., Kamel, A.E., Mohamed, S., Fakhreddine, K.: Survey of speech emotion recognition: feature, classification, schemes and databases. Elsevier 44(3), 572–587 (2011)Google Scholar
  7. 7.
    Cheng, X., Duan, Q.: Speech emotion recognition using Gaussian Mixture Model. In: In the 2nd International Conference on Computer Application and System Modeling (2012)Google Scholar
  8. 8.
    Thapliyal, N., Amoli, G.: Speech based emotion recognition with Gaussian Mixture Model. Int. J. Adv. Res. Comput. Eng. Technol. 1, 65–69 (2012)Google Scholar
  9. 9.
    Reynolds, D.: Gaussian mixture models: MIT Lincoln Laboratory, 244 St Wood, emotion recognition using support vector regression. In: 10th International Society for Music Information Retrieval Conference (ISMIR 2009)Google Scholar
  10. 10.
    Wankhade, S.B., Tijare, P., Chavhan, Y.: Speech emotion recognition system using SVM AND LIBSVM. Int. J. Comput. Sci. Appl. 4(2) (2011) ISSN: 0974-1003Google Scholar
  11. 11.
    Khanna, M.P., Kumar, S., Toscano-Medina, K., Nakano, M., Meana, H.P.: Application of vector quantization in emotion recognition from human speech. Inf. Intell. Syst. Technol. Manage. Commun. Comput. Inf. Sci. 141, 118–125 (2011)Google Scholar
  12. 12.
    Panda, B., Padhi, D., Dash, K., Mohanty, S.: Use of SVM classifier & MFCC in speech emotion recognition system. IJARCSSE 2(3) (2012) ISSN: 2277128XGoogle Scholar
  13. 13.
    Olivares-Mercado, J., Aguilar, G., Toscano-Medina, K., Nakano, M., Meana, H.P.: GMM versus SVM for face recognition and face verification. In: Corcoran, P. (ed.) Reviews, Refinements and New Ideas in Face Recognition (2011). ISBN: 978-953-307-368-2Google Scholar
  14. 14.
    Utane, A.S., Nalbalwar, S.L.: Emotion recognition through speech using gaussian mixture model and hidden markov model. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(4) (2013). ISSN: 2277 128XGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringBhubaneswar Engineering College (BEC)BhubaneswarIndia
  2. 2.Department of Electronic & Communication EngineeringGandhi Institute for Education and TechnologyBhubaneswarIndia

Personalised recommendations