Abstract
Different speech features may offer emotion specific information in different ways. This chapter explores the combination evidences offered by various speech features. In this chapter, we consider excitation source, spectral and prosodic features as specific individual speech features for classifying the emotions. Various combinations of the above mentioned individual features are explored for improving the emotion recognition performance. Since, the features are derived from different levels, the emotion specific characteristics captured by these features may be complementary or non-overlapping in nature. By properly exploiting these evidences, the recognition performance will definitely improved. From the results, its is observed that all the combinations explored in this have enhanced the recognition performance significantly.
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References
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)
S. Yildirim, M. Bulut, C.M. Lee, A. Kazemzadeh, C. Busso, Z. Deng., S. Lee, S. Narayanan, An acoustic study of emotions expressed in speech. International conference on spoken language processing (ICSLP 2004), Jeju island, Korean, Oct 2004
D. Ververidis, C. Kotropoulos, I. Pitas, Automatic emotional speech classification (ICASSP, IEEE, 2004), pp. I593–I596
F. Burkhardt, W.F. Sendlmeier, Verification of acousical correlates of emotional speech using formant-synthesis. ITRW on Speech and Emotion, Newcastle, Northern Ireland, UK, 5–7 Sept 2000, pp. 151–156
P.-Y. Oudeyer, The production and recognition of emotions in speech: features and algorithms. Int. J. Hum Comput Stud. 59, 157–183 (2003)
S.G. Koolagudi, S. Maity, V.A. Kumar, S. Chakrabarti, K.S. Rao, in IITKGP-SESC: Speech Database for Emotion Analysis. Communications in Computer and Information Science, JIIT University, Noida, 17–19 Aug 2009. Springer. ISSN: 1865–0929
R. Tato, R. Santos, R. Kompe1, J. Pardo, Emotional space improves emotion recognition. 7th International conference on spoken language processing, Denver, Colorado, USA, 16–20 Sept 2002
K. S. Rao, S. G. Koolagudi, Characterization and recognition of emotions from speech using excitation source information. Int. J. Speech Technol. 15, (Springer, Sept 2012) doi:10.1007/s10772-012-9175-2
K.S. Jarosaw Cichosz, Emotion recognition in speech signal using emotion-extracting binary decision trees, in Affective Computing and Intelligent Interfaces ACII, Lisbon, Doctoral Consortium, 13–14 Sept 2007
T. Vogt, E. Andre, Improving automatic emotion recognition from speech via gender differentiation, in Proceedings of Language Resources and Evaluation Conference (LREC), 24–26 May 2006
J. Sidorova, Speech Emotion Recognition. PhD thesis, Universitat Pompeu Fabra, 4 July 2004
B. Schuller, S. Reiter, G. Rigoll, Evolutionary feature generation in speech emotion recognition, in IEEE International Conference on Multimedia and Expo, (Toronto Ont), pp. 5–8, IEEE, 9–12 July 2006. doi:10.1109/ICME.2006.262500
S. Scherer, H. Hofmann, M. Lampmann, M. Pfeil, S. Rhinow, F. Schwenker, G. Palm, Emotion recognition from speech:stress experiment, in Proceedings of the Sixth International Language Resources and Evaluation (LREC’08) (N. C. C. Chair), ed. by K. Choukri, B. Maegaard, J. Mariani, J. Odjik, S. Piperidis, D. Tapias. (Marrakech, Morocco), European Language Resources Association (ELRA), 28–30 May 2008. ISBN: 2-9517408-4-0, http://www.lrec-conf.org/proceedings/lrec2008/
F. Ringeval, M. Chetouani, A vowel based approach for acted emotion recognition, in Verbal and Nonverbal Features of Human–Human and Human–Machine Interaction: COST Action 2102 International Conference, (Springer, Heidelberg, 2008), pp. 243–254
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Rao, K.S., Koolagudi, S.G. (2013). Robust Emotion Recognition using Combination of Excitation Source, Spectral and Prosodic Features. In: Robust Emotion Recognition using Spectral and Prosodic Features. SpringerBriefs in Electrical and Computer Engineering(). Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6360-3_4
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DOI: https://doi.org/10.1007/978-1-4614-6360-3_4
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