Feature extraction of emotions in speech using non-negative matrix factorization = NMF 알고리즘을 이용한 음성 발화에서의 감정의 특징 추출

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Recognition of emotion is an important part of communication. Also, emotional speech recognition is important for the efficient human-computer interactions. For those reasons, the importance of automatic emotional speech recognition has been emphasized and the research on this area has been increased in these days. In this study, we use AIBO feature set with non-negative matrix factorization (NMF) and support vector machine (SVM) as a classifier. With NMF, we make efficient feature set to recognize emotions. Moreover, though the speech expression of emotion is affected by language and culture area of the speaker, until now, most of researches on this area have concentrated on feature extraction and emotion recognition for one language or database. We examine the characteristics of emotional speech according to the speaker’s language and culture area. By this means, we expect the improvement of emotion recognition rate in speech, and compare the characteristics of emotional expressions in several languages and cultures.
Advisors
Lee, Soo-Youngresearcher이수영researcher
Description
한국과학기술원 : 바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2009
Identifier
327265/325007  / 020074115
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2009. 8., [ viii, 53 p. ]

Keywords

emotion recognition; feature extraction; non-negative matrix factorization; mutual information; 음성 감정 인식; 특징 추출; 상호 정보; emotion recognition; feature extraction; non-negative matrix factorization; mutual information; 음성 감정 인식; 특징 추출; 상호 정보

URI
http://hdl.handle.net/10203/27184
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=327265&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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