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Mono- and multi-lingual depression prediction based on speech processing


In this paper a mono- and multi-lingual study is presented about the depressed speech detection possibilities. Beck Depression Inventory questionnaires were used for the description of severity of depression of speakers for all languages. In the mono-lingual experiment a detailed speech parameter selection is shown, and the analysis of the connection between the severity of the depression and the calculated parameters is presented. The goal was to select the most relevant input feature vectors from a preselected set of vectors for the detection and prediction methods of depression. A detailed examination was carried out where and how to measure these features in continuous speech. After parameter selection, classification experiments were conducted on a Hungarian speech database. The overall accuracies of the classification experiments were 86%. The second part of this study concerns a multi-lingual automatic depression detection and prediction method, where three European languages were tested: German, Hungarian and Italian. With the selected quasi language-independent parameters, Support Vector Regression experiments were conducted on German, Hungarian and Italian speech databases, separately for both sexes. It was found that depression prediction based on speech signals can be achieved in a multi-lingual way. Our method is even capable of predicting the severity of depression in the case of a language not used during the training of the automatic prediction model. The experiments clearly show that multi-lingual depression recognition can be achieved, and it should be possible to construct an automated diagnostic tool for detecting depression, or for patient monitoring, in a multi-lingual way.

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We would like to thank Björn Schuller and his co-workers Jarek Krajewski and Sonja-Dana Roelena for sharing with us the database of AVEC 2013 for research purposes. They gave us the possibility to extend our multi-lingual research. We also thank Anna Esposito for the Italian depressed speech database. The research was supported by European Space Agency COALA project: Psychological Status Monitoring by Computerised Analysis of Language phenomena (COALA) (AO-11-Concordia).

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Kiss, G., Vicsi, K. Mono- and multi-lingual depression prediction based on speech processing. Int J Speech Technol 20, 919–935 (2017).

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