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Summary and Conclusions

  • K. Sreenivasa Rao
  • Shashidhar G. Koolagudi
Chapter
Part of the SpringerBriefs in Electrical and Computer Engineering book series

Abstract

This chapter summarizes the research work presented in this book, highlights the contributions of the work and discusses the scope for future work. In this book, main attention was given to emotion specific spectral and prosodic features for performing the robust emotion recognition. The book is organized into 7 chapters. The first chapter introduces speech emotion recognition as the contemporary research area. In Chap.  2 the spectral features extracted from sub-syllabic regions such as vowels, consonants and CV transition regions are proposed for robust emotion recognition from speech. Pitch synchronously extracted spectral features are also used in Chap.  2 for recognizing the emotions. Chapter  3 proposes use of dynamic prosodic features for recognition of emotions. These dynamic features along with the static prosodic features derived from sentence, word and syllable levels are used for characterizing the emotions. Emotion specific information present in different positions (initial, middle and final) of the speech utterances is used for emotion classification. In Chap.  4, combinations of various emotion specific speech features are explored for developing the robust emotion recognition systems. Chapter  5 deals with the method of multistage emotion classification using combination of features. In this chapter two stage emotion recognition system is developed using spectral and prosodic features. Chapter  6 introduces real life emotion recognition approach using different features. Chapter  7 concludes the present work and flashes light on the directions for further research.

References

  1. 1.
    S.G. Koolagudi, K.S. Rao, Emotion recognition from speech using sub-syllabic and pitch synchronous spectral features. Int. J. Speech Technol. 15, 495–511, (Springer, Dec 2012). doi: 10.1007/s10772-012-9150-8
  2. 2.
    K.S. Rao, S.G. Koolagudi, R. R. Vempada, Emotion recognition from speech using global and local prosodic features. Int. J. Speech Technol. 15, (Springer, Aug 2012). doi: 10.1007/s10772-012-9172-2
  3. 3.
    S.G. Koolagudi, K.S. Rao, Emotion recognition from speech using source, system and prosodic features. Int. J. Speech Technol. 15(3), 265–289, (Springer , 2012)Google Scholar
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    S.G. Koolagudi, K.S. Rao, Two stage emotion recognition based on speaking rate. Int. J. Speech Technol. 14, 35–48 (Mar 2011)CrossRefGoogle Scholar
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    S.G. Koolagudi, A. Barthwal, S. Devliyal, K.S. Rao, Real life emotion classification from speech using gaussian mixture models, in Communications in Computer and Information Science: Contemporary Computing, ed. by M. Parashar, D. Kaushik, O. F. Rana, R. Samtaney, Y. Yang, A. Zomaya, vol. 306 (Springer, New York, Aug 6–8 2012) pp. 250–261Google Scholar
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    S.G. Koolagudi, S. Devliyal, A. Barthwal, K.S. Rao, Emotion recognition from semi natural speech using artificial neural networks and excitation source features, in Communications in Computer and Information Science: Contemporary Computing, ed. by M. Parashar, D. Kaushik, O. F. Rana, R. Samtaney, Y. Yang, A. Zomaya, vol. 306 (Springer, New York, Aug 6–8 2012), pp. 273–282Google Scholar

Copyright information

© The Author(s) 2013

Authors and Affiliations

  1. 1.School of Information TechnologyIndian Institute of Technology KharagpurKharagpurIndia

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