# Speech-based Clinical Depression Screening: An Empirical Study

arXiv: https://arxiv.org/abs/2406.03510 · HTML: https://mlsanity.com/paper/2406.03510/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019)
- [Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of Depression](https://mlsanity.com/paper/1912.06686/) (2019)
- [Speech based Depression Severity Level Classification Using a Multi-Stage Dilated CNN-LSTM Model](https://mlsanity.com/paper/2104.04195/) (2021)
- [Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data](https://mlsanity.com/paper/2211.04924/) (2022)

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

- [Mitigating Gender Bias in Depression Detection via Counterfactual Inference](https://mlsanity.com/paper/2512.01834/) (2025)

## Abstract (arXiv)

This study investigates the utility of speech signals for AI-based depression screening across varied interaction scenarios, including psychiatric interviews, chatbot conversations, and text readings. Participants include depressed patients recruited from the outpatient clinics of Peking University Sixth Hospital and control group members from the community, all diagnosed by psychiatrists following standardized diagnostic protocols. We extracted acoustic and deep speech features from each participant's segmented recordings. Classifications were made using neural networks or SVMs, with aggregated clip outcomes determining final assessments. Our analysis across interaction scenarios, speech processing techniques, and feature types confirms speech as a crucial marker for depression screening. Specifically, human-computer interaction matches clinical interview efficacy, surpassing reading tasks. Segment duration and quantity significantly affect model performance, with deep speech features substantially outperforming traditional acoustic features.