Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals

PLoS One. 2015 Mar 23;10(3):e0120344. doi: 10.1371/journal.pone.0120344. eCollection 2015.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Emotions*
  • Phonetics*
  • Speech Recognition Software*

Grants and funding

This research is supported by a Research Grant under Fundamental Research Grant Scheme (FRGS), Ministry of Higher Education, Malaysia [Grant No: 9003-00297] and Journal Incentive Research Grants, UniMAP [Grant No: 9007-00071 and 9007-00117]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.