상세정보
학술대회
Ensemble Classifier based on Decision-Fusion of Multiple Models for Speech Emotion Recognition
- 발행일
- 201810
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1246-1248
- 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