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Depression detection based on linear and nonlinear speech features in I-vector/SVDA framework.

Shamim Mobram¹, Mansour Vali¹

¹Speech and Sound Processing Lab, Department of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.

Computers in Biology and Medicine

|August 29, 2022

Summary

This study introduces an i-vector framework for depression detection using speech features, achieving 90% accuracy in classifying depressed speakers. The system also accurately predicts depression severity, outperforming existing audio-based methods.

Keywords:

Depression detection SVDA Speech feature TEO-CB-Auto-Env i-vector

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Area of Science:

Computational linguistics
Speech processing
Mental health informatics

Background:

Depression detection from speech is crucial for mental health monitoring.
Existing i-vector frameworks require robust feature extraction and variability compensation.

Purpose of the Study:

To develop an accurate depression detection system using the i-vector framework.
To investigate the effectiveness of linear and non-linear speech features.
To enhance system generalizability with advanced compensation techniques.

Main Methods:

Utilized the i-vector framework with linear and non-linear speech features (TEO-CB-Auto-Env+Δ, Glottal+Δ, MFCC+Δ+ΔΔ).
Employed Support Vector Discriminant Analysis (SVDA) for improved feature generalizability.
Implemented decision-level fusion of multiple i-vector systems.
Predicted depression severity using Beck Depression Inventory-II (BDI-II) scores.

Main Results:

Achieved 90% accuracy in classifying depressed versus healthy speakers.
SVDA improved accuracy by up to 15.15% compared to uncompensated i-vectors.
Decision-level fusion of three feature sets yielded the best classification results.
SVDA-transformed prediction systems showed significant improvements in RMSE (29.18%) and MAE (30.34%) for BDI-II scores.

Conclusions:

The proposed i-vector framework with decision-level fusion and SVDA offers a highly accurate approach for speech-based depression detection and severity prediction.
This method surpasses previous audio-based depression analysis studies on the AVEC 2014 database.