[[1] S. Ramakrishnan and I. M. M. El Emary, “Speech emotion recognition approaches in human computer interaction,” Telecommun. Systems, vol. 52, issue 3, pp. 1467–1478, Mar. 2013. https://doi.org/10.1007/s11235-011-9624-z10.1007/s11235-011-9624-z]Search in Google Scholar
[[2] S. G. Koolagudi and K. S. Rao, “Emotion recognition from speech: a review,” Int. J. of Speech Technology, vol. 15, issue 2, pp. 99–117, June 2012. https://doi.org/10.1007/s10772-011-9125-110.1007/s10772-011-9125-1]Search in Google Scholar
[[3] Z. Xiao, E. Dellandrea, L. Chen and W. Dou, “Recognition of emotions in speech by a hierarchical approach,” in 2009 3rd Int. Conf. on Affective Computing and Intelligent Interaction and Workshops, Amsterdam, 2009, pp. 1–8. https://doi.org/10.1109/acii.2009.534958710.1109/ACII.2009.5349587]Search in Google Scholar
[[4] P. Giannoulis and G. Potamianos, “A hierarchical approach with feature selection for emotion recognition from speech,” in Proc. of the Eighth Int. Conf. on Language Resources and Evaluation, 2012, pp. 1203–1206.]Search in Google Scholar
[[5] B. Schuller, B. Vlasenko, F. Eyben, G. Rigoll and A. Wendemuth, “Acoustic Emotion Recognition: A Benchmark Comparison of Performances,” in 2009 IEEE Workshop on Automatic Speech Recognition & Understanding, Merano, 2009, pp. 552–557. https://doi.org/10.1109/asru.2009.537288610.1109/ASRU.2009.5372886]Search in Google Scholar
[[6] A. Origlia, V. Galatà and B. Ludusan, “Automatic classification of emotions via global and local prosodic features on a multilingual emotional database,” in Proc. of Speech Prosody, 2010.]Search in Google Scholar
[[7] M. Lugger, M.-E. Janoir and B. Yang, “Combining classifiers with diverse feature sets for robust speaker independent emotion recognition,” in 2009 17th European Signal Processing Conf., Glasgow, 2009, pp. 1225–1229.]Search in Google Scholar
[[8] H. Peng, F. Long and C. Ding, “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Trans. on Pattern Analysis and Machine Intelligence, pp. 1226–1238, Aug. 2005. https://doi.org/10.1109/TPAMI.2005.15910.1109/TPAMI.2005.15916119262]Search in Google Scholar
[[9] A. Mencattini, E. Martinelli, G. Costantini, M. Todisco, B. Basile, M. Bozzali and N. Di Corrado, “Speech emotion recognition using amplitude modulation parameters and a combined feature selection procedure,” Knowledge-Based Systems, vol. 63, pp. 68–81, June 2014. https://doi.org/10.1016/j.knosys.2014.03.01910.1016/j.knosys.2014.03.019]Search in Google Scholar
[[10] A. Milton and S. Tamil Selvi, “Class-specific multiple classifiers scheme to recognize emotions from speech signals,” Comput. Speech and Language, vol. 28, issue 3, pp. 727–742, May 2014. https://doi.org/10.1016/j.csl.2013.08.00410.1016/j.csl.2013.08.004]Search in Google Scholar
[[11] L. Chen, X. Mao, Y. Xue and L. L. Cheng, “Speech emotion recognition: Features and classification models,” Digital Signal Processing, pp. 1154–1160, Dec. 2012. https://doi.org/10.1016/j.dsp.2012.05.00710.1016/j.dsp.2012.05.007]Search in Google Scholar
[[12] W.-J. Yoon and K.-S. Park, “Building robust emotion recognition system on heterogeneous speech databases,” in 2011 IEEE Int. Conf. on Consumer Electronics (ICCE), Las Vegas, NV, 2011, pp. 825–826. https://doi.org/10.1109/ICCE.2011.572288610.1109/ICCE.2011.5722886]Search in Google Scholar
[[13] J. Liu, C. Chen, J. Bu, M. You and J. Tao, “Speech Emotion Recognition using an Enhanced Co-Training Algorithm,” in 2007 IEEE Int. Conf. on Multimedia and Expo, Beijing, 2007, pp. 999–1002. https://doi.org/10.1109/ICME.2007.428482110.1109/ICME.2007.4284821]Search in Google Scholar
[[14] M. Kotti and F. Paternò, “Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema,” Int. J. of Speech Technology, vol. 15, issue 2, pp. 131–150, June 2012. https://doi.org/10.1007/s10772-012-9127-710.1007/s10772-012-9127-7]Search in Google Scholar
[[15] G. Tamulevicius and T. Liogiene, “Low-order multi-level features for speech emotion recognition,” Baltic J. of Modern Computing, vol. 3, no. 4, pp. 234–247, 2015.]Search in Google Scholar
[[16] T. Liogiene and G. Tamulevicius, “Minimal cross-correlation criterion for speech emotion multi-level feature selection,” in Proc. of the Open Conf. of Electrical, Electronic and Information Sciences (eStream), Vilnius, 2015, pp. 1–4. https://doi.org/10.1109/estream.2015.711949210.1109/eStream.2015.7119492]Search in Google Scholar
[[17] F. Burkhardt, A. Paeschke, M. Rolfes, W. Sendlmeier and B. Weiss, “A database of German emotional speech,” in Proc. of Interspeech, Lissabon, 2005, pp. 1517–1520.10.21437/Interspeech.2005-446]Search in Google Scholar
[[18] J. Matuzas, T. Tišina, G. Drabavičius and L. Markevičiūtė, “Lithuanian Spoken Language Emotions Database,” Baltic Institute of Advanced Language, 2015. [Online]. Available: http://datasets.bpti.lt/lithuanian-spoken-language-emotions-database/]Search in Google Scholar
[[19] F. Eyben, M. Wollmer and B. Schuller, “OpenEAR – Introducing the munich open-source emotion and affect recognition toolkit,” in 2009 3rd Int. Conf. on Affective Computing and Intelligent Interaction and Workshops, Amsterdam, 2009, pp. 1–6. https://doi.org/10.1109/acii.2009.534935010.1109/ACII.2009.5349350]Search in Google Scholar