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.
@article{arxiv.2205.07006,
title = {Integration of Text and Graph-based Features for Detecting Mental Health Disorders from Voice},
author = {Nasser Ghadiri and Rasoul Samani and Fahime Shahrokh},
journal= {arXiv preprint arXiv:2205.07006},
year = {2022}
}