Applied Sciences (Nov 2023)

English Speech Emotion Classification Based on Multi-Objective Differential Evolution

  • Liya Yue,
  • Pei Hu,
  • Shu-Chuan Chu,
  • Jeng-Shyang Pan

DOI
https://doi.org/10.3390/app132212262
Journal volume & issue
Vol. 13, no. 22
p. 12262

Abstract

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Speech signals involve speakers’ emotional states and language information, which is very important for human–computer interaction that recognizes speakers’ emotions. Feature selection is a common method for improving recognition accuracy. In this paper, we propose a multi-objective optimization method based on differential evolution (MODE-NSF) that maximizes recognition accuracy and minimizes the number of selected features (NSF). First, the Mel-frequency cepstral coefficient (MFCC) features and pitch features are extracted from speech signals. Then, the proposed algorithm implements feature selection where the NSF guides the initialization, crossover, and mutation of the algorithm. We used four English speech emotion datasets, and K-nearest neighbor (KNN) and random forest (RF) classifiers to validate the performance of the proposed algorithm. The results illustrate that MODE-NSF is superior to other multi-objective algorithms in terms of the hypervolume (HV), inverted generational distance (IGD), Pareto optimal solutions, and running time. MODE-NSF achieved an accuracy of 49% using eNTERFACE05, 53% using the Ryerson audio-visual database of emotional speech and song (RAVDESS), 76% using Surrey audio-visual expressed emotion (SAVEE) database, and 98% using the Toronto emotional speech set (TESS). MODE-NSF obtained good recognition results, which provides a basis for the establishment of emotional models.

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