Depression Detection by Person`s Voice

Student: Zavorina Evgeniya

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Mathematical Methods of Modelling and Computer Technologies (Master)

Final Grade: 9

Year of Graduation: 2021

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 accuracies of 73.51% on DAIC-WOZ database, which compare favourably to the accuracy of 65.85% and 66.35% obtained by traditional approaches.

Full text (added May 23, 2021)

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