# Topic Modeling Based Multi-modal Depression Detection

arXiv: https://arxiv.org/abs/1803.10384 · HTML: https://mlsanity.com/paper/1803.10384/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2018.

## Built on by (most cited first, 29 of 29)

- [Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model](https://mlsanity.com/paper/2202.08210/) (2022)
- [Self-supervised representations in speech-based depression detection](https://mlsanity.com/paper/2305.12263/) (2023)
- [When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection](https://mlsanity.com/paper/2402.13276/) (2024)
- [Multi-modal Depression Estimation based on Sub-attentional Fusion](https://mlsanity.com/paper/2207.06180/) (2022)
- [Synthetic Data Generation with LLM for Improved Depression Prediction](https://mlsanity.com/paper/2411.17672/) (2024)
- [HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection](https://mlsanity.com/paper/2408.03648/) (2024)
- [U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection](https://mlsanity.com/paper/2501.09687/) (2025)
- [Two-stage Temporal Modelling Framework for Video-based Depression Recognition using Graph Representation](https://mlsanity.com/paper/2111.15266/) (2021)
- [Affect Recognition in Conversations Using Large Language Models](https://mlsanity.com/paper/2309.12881/) (2023)
- [MoodCapture: Depression Detection Using In-the-Wild Smartphone Images](https://mlsanity.com/paper/2402.16182/) (2024)
- [Automatic Depression Detection via Learning and Fusing Features from Visual Cues](https://mlsanity.com/paper/2203.00304/) (2022)
- [Context-Aware Deep Learning for Multi Modal Depression Detection](https://mlsanity.com/paper/2412.19209/) (2024)
- [Neural Architecture Searching for Facial Attributes-based Depression Recognition](https://mlsanity.com/paper/2201.09799/) (2022)
- [Discovering Mental Health Research Topics with Topic Modeling](https://mlsanity.com/paper/2308.13569/) (2023)
- [Confidence Estimation for Automatic Detection of Depression and Alzheimer's Disease Based on Clinical Interviews](https://mlsanity.com/paper/2407.19984/) (2024)
- [Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration](https://mlsanity.com/paper/2502.12204/) (2025)
- [Automatic Depression Assessment using Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/2506.18915/) (2025)
- [Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning](https://mlsanity.com/paper/2606.10796/) (2026)
- [Integration of Text and Graph-based Features for Detecting Mental Health Disorders from Voice](https://mlsanity.com/paper/2205.07006/) (2022)
- [Cost-effective Models for Detecting Depression from Speech](https://mlsanity.com/paper/2302.09214/) (2023)
- [CANAMRF: An Attention-Based Model for Multimodal Depression Detection](https://mlsanity.com/paper/2401.02995/) (2024)
- [A BERT-Based Summarization approach for depression detection](https://mlsanity.com/paper/2409.08483/) (2024)
- [Improving Speech-based Emotion Recognition with Contextual Utterance Analysis and LLMs](https://mlsanity.com/paper/2410.20334/) (2024)
- [Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarization](https://mlsanity.com/paper/2501.16106/) (2025)
- [Uncertainty Modeling in Multimodal Speech Analysis Across the Psychosis Spectrum](https://mlsanity.com/paper/2502.18285/) (2025)
- [Probabilistic Textual Time Series Depression Detection](https://mlsanity.com/paper/2511.04476/) (2025)
- [Enhancing BERTopic with Intermediate Layer Representations](https://mlsanity.com/paper/2505.06696/) (2025)
- [Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives](https://mlsanity.com/paper/2607.03744/) (2026)
- [DiaWhisper-DPO: Role-Attributed Transcription of Clinical Interviews via Failure-Mined Preference Optimization](https://mlsanity.com/paper/2609.16661/) (2026)

## Abstract (arXiv)

Major depressive disorder is a common mental disorder that affects almost 7% of the adult U.S. population. The 2017 Audio/Visual Emotion Challenge (AVEC) asks participants to build a model to predict depression levels based on the audio, video, and text of an interview ranging between 7-33 minutes. Since averaging features over the entire interview will lose most temporal information, how to discover, capture, and preserve useful temporal details for such a long interview are significant challenges. Therefore, we propose a novel topic modeling based approach to perform context-aware analysis of the recording. Our experiments show that the proposed approach outperforms context-unaware methods and the challenge baselines for all metrics.