A Novel Stochastic Transformer-based Approach for Post-Traumatic Stress
  Disorder Detection using Audio Recording of Clinical Interviews
Mamadou Dia Ghazaleh Khodabandelou Alice Othmani
Post-traumatic stress disorder (PTSD) is a mental disorder that can be
developed after witnessing or experiencing extremely traumatic events. PTSD can
affect anyone, regardless of ethnicity, or culture. An estimated one in every
eleven people will experience PTSD during their lifetime. The
Clinician-Administered PTSD Scale (CAPS) and the PTSD Check List for Civilians
(PCL-C) interviews are gold standards in the diagnosis of PTSD. These
questionnaires can be fooled by the subject's responses. This work proposes a
deep learning-based approach that achieves state-of-the-art performances for
PTSD detection using audio recordings during clinical interviews. Our approach
is based on MFCC low-level features extracted from audio recordings of clinical
interviews, followed by deep high-level learning using a Stochastic
Transformer. Our proposed approach achieves state-of-the-art performances with
an RMSE of 2.92 on the eDAIC dataset thanks to the stochastic depth, stochastic
deep learning layers, and stochastic activation function.