Enhanced depression detection from speech using Quantum Whale Optimization Algorithm for feature selection

There is an urgent need to detect depression using a non-intrusive approach that is reliable and accurate. In this paper, a simple and efficient unimodal depression detection approach based on speech is proposed, which is non-invasive, cost-effective and computationally inexpensive. A set of spectra...

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Veröffentlicht in:	Computers in biology and medicine Jg. 150; S. 106122
Hauptverfasser:	Kaur, Baljeet, Rathi, Swati, Agrawal, R.K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.11.2022 Elsevier Limited
Schlagworte:	Acoustics Algorithms Animals Biomarkers Computer applications Datasets Depression Depression - diagnosis Discriminant analysis Evolutionary algorithms Experiments Feature extraction Feature selection Frequency Genetic algorithms Humans Internal Medicine Mental depression Optimization Optimization algorithms Other Quantum-based Whale Optimization Algorithm Self evaluation Speech Statistical analysis Statistical tests Suicides & suicide attempts Support vector machines Temporal variations Whales Depression Speech Feature extraction Feature selection Quantum-based Whale Optimization Algorithm
ISSN:	0010-4825, 1879-0534, 1879-0534
Online-Zugang:	Volltext
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