참고문헌
- Simina Emerich, Eugen Lupu - Improving Speech Emotion Recognition using Frequency and Time Domain Acoustic features, EURSAIP 2011.
- Park, J.-S., J.-H. Kim and Y.-H. Oh, Feature vector classification based speech emotion recognition for service robots. IEEE Transactions on Consumer Electronics, 2009. 55(3).
- A Dictionary of Physics. 7 ed. 2015: Oxford University Press.
- Zhibing, X., Audiovisual Emotion Recognition Using Entropy estimation- based Multimodal Information Fusion. 2015, Ryerson University.
- Hinton, G. E., and Salakhutdinov, R. R.Reducing the dimensionality of data with neural networks. Science 313(5786):504-507, 2006 https://doi.org/10.1126/science.1127647
- P. Song, S. Ou, W.Zheng, Y. Jin, & L. Zhao: "Speech emotion recognition using transfer non-negative matrix factorization". In Proceedings of IEEE international conference ICASSP, pp. 5180-5184, 2016.
- Papakostas, M., et al., Recognizing Emotional States Using Speech Information, in GeNeDis 2016. 2017, Springer. p. 155-164.
- E. Ramdinmawii, A.Mohanta, V.K. Mittal: "Emotion Recognition from Speech Signal ", IEEE 10 Conference (TENCON), Malaysia, November 5-8, 2017.
- P. Shi: "Speech Emotion Recognition Based on Deep Belief Network", IEEE, 2018.
- Siddique Latif, R.R., Shahzad Younis, Junaid Qadir, Julien Epps, Transfer Learning for Improving Speech Emotion Classification Accuracy. ArXiv:1801.06353v3 [cs.CV] 2018.
- Aouani H, Ben Ayed Y: "Emotion recognition in speech using MFCC with SVM, DSVM and auto-encoder",IEEE, 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) 2018.
- L.X.Hung : Detection des emotions dans des enonces audio multilingues. Institut polytechnique de Grenoble, 2009.
- Ferrand, C: Speech science: An integrated approach to theory and clinical practice. Boston, MA: Pearson, 2007.
- Noroozi, F., et al., Vocal-based emotion recognition using random forests and decision tree. International Journal of Speech Technology, 20(2): p. 239-246, 2017. https://doi.org/10.1007/s10772-017-9396-2
- M. Swerts and E. Krahmer. Gender-related differences in the production and perception of emotion. In Proc. Interspeech, pages {334,337}, 2008.
- Eric V. Strobl & Shyam Visweswaran:' Deep Multiple Kernel Learning' ICMLA, 2013.
- http:// personal.ee.surrey.ac.uk/Personal/PJackson/SAVEE.
- Y. Ben Ayed : Detection de mots cles dans un flux de parole. These de doctorat, Ecole Nationale Superieure des Telecommunications ENST, 2003.
- DELLAAERT F., POLZIN T., WAIBEL A., "Recognizing Emotion in Speech ", Proc.of ICSLP,Philadelphie , 1996.
- L. Bottou, C. Cortes, J. Drucker, I. Guyon, Y. LeCunn, U. Muller, E. Sackinger, P. Simard et V. Vapnik : "Comparaison of classifier methods : a case study in handwriting digit recognition", dans Proceedings of the International Conference on Pattern Recognition, p.77_87, 1994.
- S. Knerr, L. Personnaz et G. Dreyfus : "Single-layer learning revisited: a stepwise procedure for building and training a neural network", Neurocomputing: Algoritms, Architectures and Applications, p.68, 1990.
- J. C. Platt, N. Cristianini et J. Shawe-Taylor : "Large margin dags for multiclass classification", dans Advances in Neural Information Processing Systems, MIT Press, 12, p.547_553, 2000.
- V. Vapnik : "Statistical learning theory", John Wiley and Sons, 1998.
- J. Weston et C. Watkins : "Support vector machines for multiclass pattern recognition", In Proceedings of the Seventh European Symposium On Artificial Neural Networks, 1999.
- A. Amina, A. Mouhamed, and C. Morad. Identification des personnes par systeme multimodale.
- Sucksmith, E., Allison, C., Baron-Cohen, S., Chakrabarti, B., & Hoekstra, R. A. Empathy and emotion recognition in people with autism, first-degree relatives, and controls. Neuropsychologia, 51(1), 98-105,2013 https://doi.org/10.1016/j.neuropsychologia.2012.11.013