  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.
