An explained artificial intelligence-based solution to identify depression severity symptoms using acoustic features

This paper represents our research to (i) propose an artificial intelligence, AI-based solution to identify depression and (ii) investigate our psychiatric knowledge. Concerning the first objective, we collected and annotated a new audio data set, and scrutinized the performance of eight regression approaches. Our studies showed that $k$-nearest neighbor and random forest form the group having the most acceptable results. Regarding our second objective, we determined the importance of the features of our best model using the SHapley Additive exPlanations approach: our findings showed that the fourth Mel-frequency cepstral coefficients, harmonic difference, and shimmer are the most important features.