Research Area:  Machine Learning

Abstract:

With the availability of voice-enabled devices such as smart phones, mental health disorders could be detected and treated earlier, particularly post-pandemic. The current methods involve extracting features directly from audio signals. In this paper, two methods are used to enrich voice analysis for depression detection: graph transformation of voice signals, and natural language processing of the transcript based on representational learning, fused together to produce final class labels. The results of experiments with the DAIC-WOZ dataset suggest that integration of text-based voice classification and learning from low level and graph-based voice signal features can improve the detection of mental disorders like depression.

Keywords:  
Text
Graph
Detect
Mental Health Disorders
Voice
natural language processing
Representational learning
Machine Learning

Author(s) Name:  Nasser Ghadiri, Rasoul Samani, Fahime Shahrokh

Publisher name:  arXiv:2205.07006

DOI:  10.48550/arXiv.2205.07006

Volume Information:  

Paper Link:   https://arxiv.org/abs/2205.07006