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 hand-crafted 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.
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Acknowledgements
The research leading to these results has received funding from the European Community’s Seventh Framework Programme through the ERC Starting Grant No. 338164 (iHEARu), and IMI RADAR-CNS under grant agreement No. 115902.
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Cummins, N., Vlasenko, B., Sagha, H., Schuller, B. (2017). Enhancing Speech-Based Depression Detection Through Gender Dependent Vowel-Level Formant Features. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_23
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DOI: https://doi.org/10.1007/978-3-319-59758-4_23
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