Open AccessSeverity Estimation of Depression Using Convolutional Neural Network
Author(s) -
Attila Zoltán Jenei,
Gábor Kiss
Publication year - 2021
Publication title -
periodica polytechnica. electrical engineering and computer science
Language(s) - English
Resource type - Journals
eISSN - 2064-5279
pISSN - 2064-5260
DOI - 10.3311/ppee.15958
Subject(s) - convolutional neural network , correlation , depression (economics) , formant , computer science , artificial intelligence , pattern recognition (psychology) , artificial neural network , psychology , speech recognition , algorithm , mathematics , geometry , vowel , economics , macroeconomics
In the present study, we attempt to estimate the severity of depression using a Convolutional Neural Network (CNN). The method is special because an auto- and cross-correlation structure has been crafted rather than using an actual image for the input of the network. The importance to investigate the possibility of this research is that depression has become one of the leading mental disorders in the world. With its appearance, it can significantly reduce an individual's quality of life even at an early stage, and in severe cases, it may threaten with suicide. It is therefore important that the disorder be recognized as early as possible. Furthermore, it is also important to determine the disorder severity of the individual, so that a treatment order can be established. During the examination, speech acoustic features were obtained from recordings. Among the features, MFCC coefficients and formant frequencies were used based on preliminary studies. From its subsets, correlation structure was created. We applied this quadratic structure to the input of a convolutional network. Two models were crafted: single and double input versions. Altogether, the lowest RMSE value (10.797) was achieved using the two features, which has a moderate strength correlation of 0.61 (between estimated and original).