Please use this identifier to cite or link to this item: http://ir.unikl.edu.my/jspui/handle/123456789/12675
metadata.conference.dc.title: Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals
metadata.conference.dc.contributor.*: Hariharan Muthusamy
Kemal Polat
Sazali Yaacob
metadata.conference.dc.date.issued: Mar-2015
metadata.conference.dc.publisher: Public Library of Science
metadata.conference.dc.identifier.citation: Muthusamy, Hariharan, Kemal Polat, and Sazali Yaacob. 2015. “Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals.” PLoS ONE 10 (3): 1–20. doi:10.1371/journal.pone.0120344.
metadata.conference.dc.description.abstract: In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature
metadata.conference.dc.description: This article index by Scopus. Sazali Yaacob (UniKL MSI)
metadata.conference.dc.identifier.uri: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0120344
http://ir.unikl.edu.my/jspui/handle/123456789/12675
metadata.conference.dc.identifier.issn: 1932-6203
Appears in Collections:Journal Articles



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