Whilst studies on emotion recognition show that gender-dependent analysis can improve emotion classification performance, the potential differences in the manifestation of depression between male and female speech have yet to be fully explored. This paper presents a qualitative analysis of phonetically aligned acoustic features to highlight differences in the manifestation of depression. Gender-dependent analysis with phonetically aligned gender-dependent features are used for speech-based depression recognition. The presented experimental study reveals gender differences in the effect of depression on vowel-level features. Considering the experimental study, we also show that a small set of knowledge-driven gender-dependent vowel-level features can outperform state-of-the-art turn-level acoustic features when performing a binary depressed speech recognition task. A combination of these preselected gender-dependent vowel-level features with turn-level standardised openSMILE features results in additional improvement for depression recognition.
Cite as: Vlasenko, B., Sagha, H., Cummins, N., Schuller, B. (2017) Implementing Gender-Dependent Vowel-Level Analysis for Boosting Speech-Based Depression Recognition. Proc. Interspeech 2017, 3266-3270, doi: 10.21437/Interspeech.2017-887
@inproceedings{vlasenko17_interspeech,
author={Bogdan Vlasenko and Hesam Sagha and Nicholas Cummins and Björn Schuller},
title={{Implementing Gender-Dependent Vowel-Level Analysis for Boosting Speech-Based Depression Recognition}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={3266--3270},
doi={10.21437/Interspeech.2017-887}
}