Multi-Local Attention for Speech-Based Depression Detection

Tao, F., Ge, X., Ma, W., Esposito, A. and Vinciarelli, A. (2023) Multi-Local Attention for Speech-Based Depression Detection. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023), Rhodes, Greece, 4-10 June 2023, ISBN 9781728163277 (doi: 10.1109/ICASSP49357.2023.10095757)

Abstract

This article shows that an attention mechanism, the Multi-Local Attention, can improve a depression detection approach based on Long Short-Term Memory Networks. Besides leading to higher performance metrics (e.g., Accuracy and F1 Score), Multi-Local Attention improves two other aspects of the approach, both important from an application point of view. The first is the effectiveness of a confidence score associated to the detection outcome at identifying speakers more likely to be classified correctly. The second is the amount of speaking time needed to classify a speaker as depressed or non-depressed. The experiments were performed over read speech and involved 109 participants (including 55 diagnosed with depression by professional psychiatrists). The results show accuracies up to 88.0% (F1 Score 88.0%).

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Tao, Mr Fuxiang and Ge, Ms Xuri and ma, wei and Vinciarelli, Professor Alessandro
• Authors: Tao, F., Ge, X., Ma, W., Esposito, A., and Vinciarelli, A.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781728163277
• Published Online: 05 May 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy