DC Field | Value | Language |
---|---|---|
dc.contributor.author | 곽현석 | - |
dc.contributor.author | 김수현 | - |
dc.contributor.author | 곽윤근 | - |
dc.date.accessioned | 2013-03-16T23:27:57Z | - |
dc.date.available | 2013-03-16T23:27:57Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2002 | - |
dc.identifier.citation | 대한소음진동학회 2002년도 추계학술대회, v.2, no., pp.762 - 766 | - |
dc.identifier.uri | http://hdl.handle.net/10203/137266 | - |
dc.description.abstract | This paper describes the emotion recognition algorithm using HMM(Hidden Markov Model) method. The relation between the mechanic system and the human has just been unilateral so far. This is the why people don't want to get familiar with multi-service robots of today. If the function of the emotion recognition is granted to the robot system, the concept of the mechanic part will be changed a lot. Pitch and Energy extracted from the human speech are good and important factors to classify the each emotion (neutral, happy, sad and angry etc.), which are called prosodic features. HMM is the powerful and effective theory among several methods to construct the statistical model with characteristic vector which is made up with the mixture of prosodic features | - |
dc.language | KOR | - |
dc.publisher | 대한소음진동학회 | - |
dc.title | 운율 특성 벡터와 가우시안 혼합 모델을 이용한 감정인식 | - |
dc.title.alternative | Emotion Recognition using Prosodic Feature Vector and Gaussian Mixture Model | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.volume | 2 | - |
dc.citation.beginningpage | 762 | - |
dc.citation.endingpage | 766 | - |
dc.citation.publicationname | 대한소음진동학회 2002년도 추계학술대회 | - |
dc.identifier.conferencecountry | South Korea | - |
dc.identifier.conferencecountry | South Korea | - |
dc.contributor.localauthor | 김수현 | - |
dc.contributor.localauthor | 곽윤근 | - |
dc.contributor.nonIdAuthor | 곽현석 | - |
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