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

delete2022-11-01
delete14
PRE
AI
B
Baljeet Kaur
S
Swati Rathi *
R
R. K. Agrawal
DOI:10.1016/j.compbiomed.2022.106122delete
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Abstract

Abstract

En 中文
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 spectral, temporal and spectro-temporal fea-tures is derived from the speech signal of healthy and depressed subjects. To select a minimal subset of the relevant and non-redundant speech features to detect depression, a two-phase approach based on the nature-inspired wrapper-based feature selection Quantum-based Whale Optimization Algorithm (QWOA) is proposed. Experiments are performed on the publicly available Distress Analysis Interview Corpus Wizard-of-Oz (DAIC-WOZ) dataset and compared with three established univariate filtering techniques for feature selection and four well-known evolutionary algorithms. The proposed model outperforms all the univariate filter feature selection techniques and the evolutionary algorithms. It has low computational complexity in comparison to traditional wrapper-based evolutionary methods. The performance of the proposed approach is superior in comparison to existing unimodal and multimodal automated depression detection models. The combination of spectral, tem-poral and spectro-temporal speech features gave the best result with the LDA classifier. The performance ach-ieved with the proposed approach, in terms of F1-score for the depressed class and the non-depressed class and error is 0.846, 0.932 and 0.094 respectively. Statistical tests demonstrate that the acoustic features selected using the proposed approach are non-redundant and discriminatory. Statistical tests also establish that the performance of the proposed approach is significantly better than that of the traditional wrapper-based evolutionary methods.
Keywords:
Depression
Speech
Feature extraction
Feature selection
Quantum-based Whale Optimization Algorithm

Journal

Computers in Biology and Medicine cover
Computers in Biology and Medicine
IF:
6.3
Papers:
8.3K
Citations:
3.3W

Organization

U
university of delhi
Scholars:
1.2W
Papers: 9.6K
Citations: 3
J
jawaharlal nehru university, new delhi
Scholars:
3.8K
Papers: 3.5K
Citations: 2