Depression detection using cascaded attention based deep learning framework using speech data

doi:10.1007/s11042-023-18076-w

Return

Depression detection using cascaded attention based deep learning framework using speech data

2024-01-22

PRE

Sachi Gupta

Shivani Agarwal

Dilkeshwar Pandey

DOI：10.1007/s11042-023-18076-w

Original

Original request for help

Save

Abstract

En 中文

Efficient detection of depression is a challenging scenario in the field of speech signal processing. Since the speech signals provide a better diagnosis of depression, a significant methodology is required for detection. However, manual examination performed by radiologists can be time-consuming and may not be feasible in complex circumstances. Diverse detection methodologies have been proposed previously, but they are found to be less accurate, time-consuming and lead over maximized error rates. The proposed research article presents an effective and automatic deep learning-based depression detection using speech signal data. The steps involved in depression prediction are data acquisition, pre-processing, Feature Extraction, Feature selection and classification. The initial step in depression detection is data acquisition, which aims at collecting speech signals from the Distress Analysis Interview Corpus (DAIC-WOZ) and Sonde Health-free speech (SH2-FS) datasets. The collected data are pre-processed through MS_DWT (Multi-stage Discrete Wavelet Transform) to offer noise-free signals and improved signal quality. The relevant features required for processing the speech signal are extracted through Hilbert Huang (H-H) transform linear prediction cepstrum coefficient (LPCC), fundamental frequency, formants, speaking rate and Mel frequency cepstral coefficients (MFCC). From the extracted features, ideal features required for enhancing the detection accuracy are selected using the Price Auction optimization algorithm (PAOA). Finally, the depression and non-depression states are classified using deep convolutional Attention Cascaded two directional long short-term memory (DAttn_Conv 2D LSTM) with a softmax classifier. The overall accuracy obtained in classifying the depressed and non-depressed classes is 97.82% and 98.91%, respectively.

Keywords:

Speech signals

Multi-stage Discrete Wavelet Transform

Auction Optimization

Deep convolutional Attention

Depression

And Non-depression