Automatic assessment of depression from speech signals is affected by variabilities in acoustic content and speakers. In this study, we focused on addressing these variabilities. We used a database comprised of recordings of interviews from a large number of female speakers: 735 individuals suffering from depressive (dysthymia and major depression) and anxiety disorders (generalized anxiety disorder, panic disorder with or without agoraphobia) and 953 healthy individuals. Leveraging this unique and extensive database, we built an i-vector framework. In order to capture various aspects of speech signals, we used voice quality features in addition to conventional cepstral features. The features (F0, F1, F2, F3, H1-H2, H2-H4, H4-H2k, A1, A2, A3 and CPP) were inspired by a psychoacoustic model of voice quality [1]. An i-vector-based system using Mel Frequency Cepstral Coefficients (MFCCs) and another using voice quality features was developed. Voice quality features performed as well as MFCCs. A score-level fusion was then used to combine these two systems, resulting in a 6% relative improvement in accuracy in comparison with the i-vector system based on MFCCs alone. The system was robust even when the duration of the utterances was shortened to 10 seconds.
Cite as: Afshan, A., Guo, J., Park, S.J., Ravi, V., Flint, J., Alwan, A. (2018) Effectiveness of Voice Quality Features in Detecting Depression. Proc. Interspeech 2018, 1676-1680, doi: 10.21437/Interspeech.2018-1399
@inproceedings{afshan18_interspeech,
author={Amber Afshan and Jinxi Guo and Soo Jin Park and Vijay Ravi and Jonathan Flint and Abeer Alwan},
title={{Effectiveness of Voice Quality Features in Detecting Depression}},
year=2018,
booktitle={Proc. Interspeech 2018},
pages={1676--1680},
doi={10.21437/Interspeech.2018-1399}
}