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Conference Paper Ensemble Classifier based on Decision-Fusion of Multiple Models for Speech Emotion Recognition
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Authors
Kyoungju Noh, Jiyoun Lim, Seungeun Chung, Gague Kim, Hyuntae Jeong
Issue Date
2018-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1246-1248
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539502
Abstract
This paper proposes an ensemble classifier based on decision-fusion of multiple SER (Speech Emotion Recognition) models. The one of the multiple SER models used in this work is a typical categorical learning model for classifying the emotion labels, while the others are A/V (Arousal/Valence) models that recognize multiple A/V states based on the Russell's A/V emotion space. The evaluation performed in this work shows that the SER accuracy of the proposed ensemble classifier that combines each output of categorical model and A/V models is improved compare to the result when each SER model is applied separately.
KSP Keywords
Categorical model, Multiple models, Speech Emotion recognition, ensemble classifiers, learning models