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Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals
Joint Authors
Polat, Kemal
Muthusamy, Hariharan
Yaacob, Sazali
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-02
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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 k-nearest neighbor (kNN) 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.
American Psychological Association (APA)
Muthusamy, Hariharan& Polat, Kemal& Yaacob, Sazali. 2015. Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1073727
Modern Language Association (MLA)
Muthusamy, Hariharan…[et al.]. Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals. Mathematical Problems in Engineering No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1073727
American Medical Association (AMA)
Muthusamy, Hariharan& Polat, Kemal& Yaacob, Sazali. Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1073727
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1073727