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학술대회 Ensemble Classifier based on Decision-Fusion of Multiple Models for Speech Emotion Recognition
Cited 3 time in scopus Download 11 time Share share facebook twitter linkedin kakaostory
저자
노경주, 임지연, 정승은, 김가규, 정현태
발행일
201810
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1246-1248
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539502
협약과제
18ZS1100, 자율성장형 AI 핵심원천기술 연구, 이윤근
초록
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 제안 키워드
Categorical model, Learning model, Multiple models, Speech Emotion recognition, ensemble classifier