Anger or Joy? Emotion Recognition Using Nonlinear Dynamics of Speech

REFERENCES

• Albornoz, E. M., D. H. Milone, and H. L. Rufiner. 2011. Spoken emotion recognition using hierarchical classifiers. Computer Speech & Language 25:556–70. doi:10.1016/j.csl.2010.10.001.
   Web of Science ®Google Scholar
• Altun, H., and G. Polat. 2009. Boosting selection of speech related features to improve performance of multi-class SVMs in emotion detection. Expert Systems with Applications 36:8197–203. doi:10.1016/j.eswa.2008.10.005.
   Web of Science ®Google Scholar
• Bishop, C. 2006. Pattern recognition and machine learning. New York, NY: Springer.
   Google Scholar
• Bitouk, D., R. Verma, and A. Nenkova. 2010. Class-level spectral features for emotion recognition. Speech Communication 52:613–25. doi:10.1016/j.specom.2010.02.010.
   PubMed Web of Science ®Google Scholar
• Buck, R. 1988. Human motivation and emotion. New York, NY: Wiley.
   Google Scholar
• Burkhardt, F., A. Paeschke, M. Rolfes, W. Sendlmeier, and B. Weiss (2005). A database of German emotional speech. In Proceedings of Interspeech, 1517–20. ISCA.
   Google Scholar
• Cowie, R., E. Douglas-Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fellenz, and J. Taylor. 2001. Emotion recognition in human–computer interaction. IEEE Signal Processing Magazine 18 (1):32–80. doi:10.1109/79.911197.
   Web of Science ®Google Scholar
• Drugman, T., B. Bozkurt, and T. Dutoit. 2011. Causal-anticausal decomposition of speech using complex cepstrum for glottal source estimation. Speech Communication 53 (6):855–66. doi:10.1016/j.specom.2011.02.004.
   Web of Science ®Google Scholar
• Duda, R. O., P. E. Hart, and D. G. Stork. 2001. Pattern classification. New York, NY: John Wiley & Sons.
   Google Scholar
• El Ayadi, M., M. S. Kamel, and F. Karray. 2011. Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition 44:572–87.
   Web of Science ®Google Scholar
• Fraser, A. M.. 1989. Information and entropy in strange attractors. IEEE Transactions Informatics Theory 35 (2):245–62.
   Web of Science ®Google Scholar
• Gen, M., and R. Cheng. 2000. Genetic algorithms and engineering optimization. vol. 68. New York, NY: Wiley Interscience Publication.
   Google Scholar
• Gonzalez, S., and M. Brookes. 2011. A pitch estimation filter robust to high levels of noise (PEFAC). In Proceedings of EUSIPCO, Barcelona, Spain, August 29–September 2, 2011.
   Google Scholar
• Grimm, M., K. Kroschel, and S. Narayanan. 2007. Support vector regression for automatic recognition of spontaneous emotions in speech. In Proceedings of the international conference on acoustics, speech and signal processing, vol. 4:1085–88. ICASSP/IEEE.
   Google Scholar
• Guyon, I., and A. Elisseeff. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research 3:1157–82.
   Google Scholar
• He, L., M. Lech, N. C. Maddage, and N. B. Allen. 2011. Study of empirical mode decomposition and spectral analysis for stress and emotion classification in natural speech. Biomedical Signal Processing and Control 6:139–46.
   Web of Science ®Google Scholar
• Hong-guang, M. A., and H. Chong-Zhao. 2006. Selection of embedding dimension and delay time in phase space reconstruction. Frontiers of Electrical and Electronic Engineering China 1:111–14.
   Google Scholar
• Hsu, C. C., C. C. Chang, and C. J. Lin. 2007. A practical guide to support vector classification (Technical Report Department of Computer Science, National Taiwan University, Taiwan).
   Google Scholar
• Huang, X., A. Acero, and H. S. Hon. 2001. Spoken language processing: A guide to theory, algorithm, and system development. Upper Saddle River, NJ: Prentice Hall.
   Google Scholar
• Indrebo, K. M., R. J. Povinelli, and M. T. Johnson. 2006. Sub-banded reconstructed phase spaces for speech recognition. Speech Communication 48:760–74.
   Web of Science ®Google Scholar
• Johnson, M. T., A. C. Lindgren, R. J. Povinelli, and X. Yuan. (2003). Performance of nonlinear speech enhancemeny using phase space reconstruction. In Proceedings of the international conference on acoustics, speech, and signal processing, 2003, 872–75. ICASSP/IEEE.
   Google Scholar
• Kaiser, J.. 1990. On a simple algorithm to calculate the ‘energy’ of a signal. In Proceedings of the international conference on acoustics, speech and signal processing, 1990, vol. 1:381–84. ICASSP/IEEE.
   Google Scholar
• Kamaruddin, N., A. Wahab, and C. Quek. 2011. Cultural dependency analysis for understanding speech emotion. Expert Systems with Applications 11:028.
   Google Scholar
• Kim, E. H., K. H. Hyun, S. H. Kim, and Y. K. Kwak. 2009. Improved emotion recognition with a novel speaker-independent feature. IEEE/ASME Transactions on Mechatronics 14 (3):317–25.
   Web of Science ®Google Scholar
• Kotti, M., and C. Kotropoulos (2008). Gender classification in two emotional speech databases. Paper presented at 19th International Conference on Pattern Recognition, ICPR 2008, Tampa, Florida, December 8–11.
   Google Scholar
• Krajewski, J., S. Schnieder, D. Sommer, A. Batliner, and B. Schuller. 2012. Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech. Neurocomputing 84:65–75.
   Web of Science ®Google Scholar
• Laukka, P., D. Neiberg, M. Forsell, I. Karlsson, and K. Elenius. 2011. Expression of affect in spontaneous speech: Acoustic correlates and automatic detection of irritation and resignation. Computer Speech and Language 25:84–104.
   Web of Science ®Google Scholar
• Lee, C. C., E. Mower, C. Busso, S. Lee, and S. Narayanan. 2011. Emotion recognition using a hierarchical binary decision tree approach. Speech Communication 53:1162–71.
   Web of Science ®Google Scholar
• Polzehl, T., A. Schmitt, F. Metze, and M. Wagner. 2011. Anger recognition in speech using acoustic and linguistic cues. Speech Communication 53:1198–209.
   Web of Science ®Google Scholar
• Prajith, P. (2008). Investigation on the applications of dynamical instabilities and deterministic chaos for speech signal processing (PhD thesis, University of Calicut).
   Google Scholar
• Rong, J., G. Li, and Y. P. Phoebe Chen. 2009. Acoustic feature selection for automatic emotion recognition from speech. Information Processing and Management 45:315–28.
   Web of Science ®Google Scholar
• Sauer, T., J. A. Yorke, and M. Casdagli. 1991. Embedology. Journal Statistical Physics 65: 579–616.
   Web of Science ®Google Scholar
• Schuller, B., M. Wimmer, L. M. Osenlechner, C. Kern, and G. Rigoll (2008). Brute-forcing hierarchical functional for paralinguistics: A waste of feature space? In Proceedings of the international conference on acoustics, speech, and signal processing, vol. 33:4501–04. ICASSP/IEEE, Las Vegas, Nevada.
   Google Scholar
• Sima, C., and E. R. Dougherty. 2008. The peaking phenomenon in the presence of feature-selection. Pattern Recognition Letters 29:1667–74.
   Web of Science ®Google Scholar
• Sun, J., N. Zheng1, and X. Wang. 2007. Enhancement of Chinese speech based on nonlinear dynamics. Signal Processing 87: 2431–45.
   Web of Science ®Google Scholar
• Takens, F.. 1981. Detecting strange attractors in turbulence. In Dynamical systems and turbulence, Warwick, 1980, ed. D. Rand and L. S. Young, 898: 366–81. Berlin, Heidelberg: Springer
   Google Scholar
• Teager, H. M., and S. M. Teager. 1989. Evidence for nonlinear sound production mechanisms in the vocal tract. Speech production and speech modelling. In NATO advanced study institute series D, eds. W. J. Hardcastle, and A. Marchal, vol. 55. France: Bonas.
   Google Scholar
• Theodoridis, S., and K. Koutroumbas. 2008. Pattern recognition. Florida: Elsevier.
   Google Scholar
• Vapnik, V.. 1995. The nature of statistical learning theory. New York, NY: Springer.
   Google Scholar
• Wu, S., T. H. Falk, and W. Y. Chan. 2011. Automatic speech emotion recognition using modulation spectral features. Speech Communication 53:768–85.
   Web of Science ®Google Scholar
• Yang, B., and M. Lugger. 2010. Emotion recognition from speech signals using new harmony features. Signal Processing 90:1415–23.
   Web of Science ®Google Scholar
• Zhou, G., J. Hansen, and J. Kaiser. 2001. Nonlinear feature based classification of speech under stress. IEEE Transactions on Audio Speech Language Processing 9:201–16.
   Web of Science ®Google Scholar