@inproceedings{2191b131db7443838f3fc70bedca429f,
title = "Enhancing speech-based depression detection through gender dependent vowel-level formant features",
abstract = "Depression has been consistently linked with alterations in speech motor control characterised by changes in formant dynamics. However, potential differences in the manifestation of depression between male and female speech have not been fully realised or explored. This paper considers speech-based depression classification using gender dependant features and classifiers. Presented key observations reveal gender differences in the effect of depression on vowel-level formant features. Considering this observation, we also show that a small set of handcrafted gender dependent formant features can outperform acoustic-only based features (on two state-of-the-art acoustic features sets) when performing two-class (depressed and non-depressed) classification.",
keywords = "Depression, Gender, Speech motor control Classification, Vowel-level formants",
author = "Nicholas Cummins and Bogdan Vlasenko and Hesam Sagha and Bj{\"o}rn Schuller",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 16th Conference on Artificial Intelligence in Medicine, AIME 2017 ; Conference date: 21-06-2017 Through 24-06-2017",
year = "2017",
doi = "10.1007/978-3-319-59758-4_23",
language = "English",
isbn = "9783319597577",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "209--214",
editor = "{[surname]ten Teije}, Annette and Christian Popow and Lucia Sacchi and Holmes, {John H.}",
booktitle = "Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings",
}