Author
Demiroğlu, Cenk, Besirli, A., Özkanca, Yasin Sedar, Celik, S.
Publication Date
2020-11-17
Publication Place
-
Springer Nature
Subject
Depression detection, Acoustic features, Feature selection
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1687-4722
Record ID
e2d767e8-fb6d-422f-86cd-6256f0a9d516
Library Location
Electrical & Electronics Engineering
Date
2020-11-17
Sample Text
Depression is a widespread mental health problem around the world with a significant burden on economies. Its early diagnosis and treatment are critical to reduce the costs and even save lives. One key aspect to achieve that goal is to use technology and monitor depression remotely and relatively inexpensively using automated agents. There has been numerous efforts to automatically assess depression levels using audiovisual features as well as text-analysis of conversational speech transcriptions. However, difficulty in data collection and the limited amounts of data available for research present challenges that are hampering the success of the algorithms. One of the two novel contributions in this paper is to exploit databases from multiple languages for acoustic feature selection. Since a large number of features can be extracted from speech, given the small amounts of training data available, effective data selection is critical for success. Our proposed multi-lingual method was effective at selecting better features than the baseline algorithms, which significantly improved the depression assessment accuracy. The second contribution of the paper is to extract text-based features for depression assessment and use a novel algorithm to fuse the text- and speech-based classifiers which further boosted the performance.
DOI
10.1186/s13636-020-00182-4
Cilt
2020
