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Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Authors :
Liya Yue
Pei Hu
Jiulong Zhu
Source :
Biomimetics, Vol 9, Iss 6, p 360 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender–emotion model. First, gender and emotion features were extracted from voice signals to lay the foundation for our recognition model. Second, a genetic algorithm (GA) processed high-dimensional features, and the Fisher score was used for evaluation. Third, features were ranked by their importance, and the GA was improved through novel crossover and mutation methods based on feature importance, to improve the recognition accuracy. Finally, the proposed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due to subtle vocal differences, overlapping pitch and tone variability, and similar prosodic features. Notably, the primary features for gender-based differentiation mainly involved mel frequency cepstral coefficients (MFCC) and log MFCC.

Details

Language :
English
ISSN :
23137673
Volume :
9
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Biomimetics
Publication Type :
Academic Journal
Accession number :
edsdoj.8896d9317b34fe1b22165f809fbb437
Document Type :
article
Full Text :
https://doi.org/10.3390/biomimetics9060360