Depression score estimating method using acoustic features of speech utterances

Abstract

In this paper, we propose a method to estimate speaker's depression score using acoustic features of his/her speech. 150 speech utterances that 15 subjects read 10 types of sentences were recorded as training data, and the depression scores of the subjects were calculated by Beck Depression Inventory (BDI) just after the recording. Acoustic features are calculated by using openSMILE or Surfboard, and Support Vector Regression or LightGBM are used for machine learning procedure. The experimental results showed that the estimated depression scores obtained a correlate efficient of 0.932 with the correct answer.