Abstract
In this work, a machine learning algorithm is proposed to detect depression. The Transformer encoder network is considered and compared with top baseline approaches. Low-level features are extracted from audio recordings and then are augmented to overcome the problem of the small size of available dataset. The Transformer network achieves recognition accuracy of 73.51% on DAIC-WOZ database, which compare favourably to the accuracy of 65.85% and 66.35% obtained by traditional approaches.
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CITATION STYLE
Zavorina, E., & Makarov, I. (2022). Depression Detection by Person’s Voice. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13217 LNCS, pp. 250–262). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16500-9_21
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