# Integration of Text and Graph-based Features for Detecting Mental Health Disorders from Voice

arXiv: https://arxiv.org/abs/2205.07006 · HTML: https://mlsanity.com/paper/2205.07006/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 14 May 2022.

## Builds on (most cited first, 3 of 3)

- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Topic Modeling Based Multi-modal Depression Detection](https://mlsanity.com/paper/1803.10384/) (2018)
- [Gender Bias in Depression Detection Using Audio Features](https://mlsanity.com/paper/2010.15120/) (2020)

## Built on by (most cited first, 1 of 1)

- [Leveraging Audio and Text Modalities in Mental Health: A Study of LLMs Performance](https://mlsanity.com/paper/2412.10417/) (2024)

## Abstract (arXiv)

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.