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http://dx.doi.org/10.7471/ikeee.2020.24.4.998

A Training Method for Emotion Recognition using Emotional Adaptation  

Kim, Weon-Goo (Dept. of Electrical Engineering, Kunsan National University)
Publication Information
Journal of IKEEE / v.24, no.4, 2020 , pp. 998-1003 More about this Journal
Abstract
In this paper, an emotion training method using emotional adaptation is proposed to improve the performance of the existing emotion recognition system. For emotion adaptation, an emotion speech model was created from a speech model without emotion using a small number of training emotion voices and emotion adaptation methods. This method showed superior performance even when using a smaller number of emotional voices than the existing method. Since it is not easy to obtain enough emotional voices for training, it is very practical to use a small number of emotional voices in real situations. In the experimental results using a Korean database containing four emotions, the proposed method using emotional adaptation showed better performance than the existing method.
Keywords
emotion recognition; emotional speech; emotional adaptation; GMM; speech parameter;
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1 Rafael A. Calvo, SSidney D'Mello, "Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications," IEEE Transactions on the. Affective Computing, Vol.1, No.1, pp.18-37, 2010. DOI: 10.1109/T-AFFC.2010.1   DOI
2 V. Kostv and S. Fukuda, "Emotion in User Interface, Voice Interaction System," in Proc.. of the IEEE International Conference on Systems, Cybernetics Representation, pp.798-803, 2000.
3 T. Moriyama and S. Oazwa, "Emotion Recognition and Synthesis System on Speech," in Proc. of the IEEE Intl. Conference on Multimedia Computing and System, pp.840-844, 1999. DOI: 10.1109/MMCS.1999.779310   DOI
4 L. C. Siva and P. C. Ng, "Bimodal Emotion Recognition," in Proc. of the 4th Intl. Conference on Automatic Face and Gesture Recognition, pp.332-335, 2000. DOI: 10.1109/AFGR.2000.840655   DOI
5 Kokane Amol T., Ram Mohana Reddy Guddeti, "Multiclass SVM-based Language- Independent Emotion Recognition using Selective Speech Features," in Proc. of ICACCI, pp.1069-1073, 2014. DOI: 10.1109/ICACCI.2014.6968337   DOI
6 Rode Snehal Sudhkar, Manjare Chandraprabha Anil, "Analysis of Speech Features for Emotion Detection: A review," in Proc. of the 2015 International Conference on Computing Communication Control and Automation, pp.661-664, 2015. DOI: 10.1109/ICCUBEA.2015.135   DOI
7 Y. G. Kim, Y. C. Bae, "Design of Emotion Recognition Model Using fuzzy Logic," in Proc. of KFIS Spring Conference, pp.268-282, 2000. DOI: 10.1007/s11042-019-7250-z   DOI
8 Natalia Tomashenko1,2 and Yannick Esteve, "Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models," in Proc. of the Eleventh International Conference on Language Resources, pp.3163-3170. 2018.
9 K. B. Sim, C. H. Park, "Analyzing the Element of Emotion Recognition from Speech," Journal of Korean Institute of Intelligent Systems, Vol.11, No.6, pp.510-515, 2001. DOI: 10.5391/JKIIS.2003.13.1.045   DOI
10 Reynolds, D. A., Quatieri, T. F., Dunn, R. B., "Speaker Verification using Adapted Gaussian Mixture Models," Digital Signal Processing, Vol.10, pp.19-41, 2000. DOI: 10.1006/dspr.1999.0361   DOI
11 W. G. Kim, "Speech Emotion Recognition using Feature Selection and Fusion Method," Transactions of the Korean Institude of Electrical Engineers, Vol.66, No.8, pp.1265-1271, 2017. DOI: 10.5370/KIEE.2017.66.8.1265   DOI