감성 인식을 위한 강화학습 기반 상호작용에 의한특징선택 방법 개발Reinforcement Learning Method Based Interactive Feature Selection(IFS) Method for Emotion Recognition
- Authors
- 박창현; 심귀보
- Issue Date
- 2006
- Publisher
- 제어·로봇·시스템학회
- Keywords
- reinforcement learning; feature selection; emotion recognition; speech signal
- Citation
- 제어.로봇.시스템학회 논문지, v.12, no.7, pp 666 - 670
- Pages
- 5
- Journal Title
- 제어.로봇.시스템학회 논문지
- Volume
- 12
- Number
- 7
- Start Page
- 666
- End Page
- 670
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/28990
- ISSN
- 1976-5622
- Abstract
- This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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