Please use this identifier to cite or link to this item: http://ir.unikl.edu.my/jspui/handle/123456789/12677
metadata.conference.dc.title: Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals
metadata.conference.dc.contributor.*: Hariharan Muthusamy
Kemal Polat
Sazali Yaacob
metadata.conference.dc.date.issued: 2015
metadata.conference.dc.publisher: Hindawi Publishing Corporation
metadata.conference.dc.identifier.citation: Hariharan Muthusamy, Kemal Polat, and Sazali Yaacob, “Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals,” Mathematical Problems in Engineering, vol. 2015, Article ID 394083, 13 pages, 2015. doi:10.1155/2015/394083
metadata.conference.dc.description.abstract: Recently, researchers have paid escalating attention to studying the emotional state of an individual from his/her speech signals as the speech signal is the fastest and the most natural method of communication between individuals. In this work, new feature enhancement using Gaussian mixture model (GMM) was proposed to enhance the discriminatory power of the features extracted from speech and glottal signals. Three different emotional speech databases were utilized to gauge the proposed methods. Extreme learning machine (ELM) and -nearest neighbor (NN) classifier were employed to classify the different types of emotions. Several experiments were conducted and results show that the proposed methods significantly improved the speech emotion recognition performance compared to research works published in the literature.
metadata.conference.dc.description: This article index by SCOPUS. Sazali Yaacob (UniKL MSI)
metadata.conference.dc.identifier.uri: http://www.hindawi.com/journals/mpe/2015/394083/cta/
http://ir.unikl.edu.my/jspui/handle/123456789/12677
metadata.conference.dc.identifier.issn: 1024-123X
Appears in Collections:Journal Articles



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