# HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection

arXiv: https://arxiv.org/abs/2408.03648 · HTML: https://mlsanity.com/paper/2408.03648/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 92 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 20 of 20)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2005.03545/) (2020)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [Transformer-based Acoustic Modeling for Hybrid Speech Recognition](https://mlsanity.com/paper/1910.09799/) (2019)
- [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019)
- [AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge](https://mlsanity.com/paper/1605.01600/) (2016)
- [Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model](https://mlsanity.com/paper/2202.08210/) (2022)
- [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)
- [SpeechFormer: A Hierarchical Efficient Framework Incorporating the Characteristics of Speech](https://mlsanity.com/paper/2203.03812/) (2022)
- [DEPA: Self-Supervised Audio Embedding for Depression Detection](https://mlsanity.com/paper/1910.13028/) (2019)
- [Automatic Depression Detection via Learning and Fusing Features from Visual Cues](https://mlsanity.com/paper/2203.00304/) (2022)
- [Depression Scale Recognition from Audio, Visual and Text Analysis](https://mlsanity.com/paper/1709.05865/) (2017)
- [Learning Co-Speech Gesture for Multimodal Aphasia Type Detection](https://mlsanity.com/paper/2310.11710/) (2023)

## Built on by (most cited first, 12 of 12)

- [MLlm-DR: Towards Explainable Depression Recognition with MultiModal Large Language Models](https://mlsanity.com/paper/2507.05591/) (2025)
- [Towards Stable Cross-Domain Depression Recognition under Missing Modalities](https://mlsanity.com/paper/2512.06447/) (2025)
- [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)
- [It Hears, It Sees too: Multi-Modal LLM for Depression Detection By Integrating Visual Understanding into Audio Language Models](https://mlsanity.com/paper/2511.19877/) (2025)
- [Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning](https://mlsanity.com/paper/2606.10796/) (2026)
- [Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarization](https://mlsanity.com/paper/2501.16106/) (2025)
- [Innovative Framework for Early Estimation of Mental Disorder Scores to Enable Timely Interventions](https://mlsanity.com/paper/2502.03965/) (2025)
- [CAtCh: Cognitive Assessment through Cookie Thief](https://mlsanity.com/paper/2506.06603/) (2025)
- [Tri-Modal Severity Fused Diagnosis across Depression and Post-traumatic Stress Disorders](https://mlsanity.com/paper/2510.20239/) (2025)
- [HiMA-MDD: A Hierarchical Multi-Agent Harness for Interpretable Multimodal Depression Detection in Clinical Interviews](https://mlsanity.com/paper/2608.21868/) (2026)
- [TAMI: Temporally Aligned, Missingness-Aware, and Interpretable Multimodal Fusion for Mental Health Assessment in Older Adults with Mild Cognitive Impairment](https://mlsanity.com/paper/2608.30857/) (2026)

## Abstract (arXiv)

The utilization of automated depression detection significantly enhances early intervention for individuals experiencing depression. Despite numerous proposals on automated depression detection using recorded clinical interview videos, limited attention has been paid to considering the hierarchical structure of the interview questions. In clinical interviews for diagnosing depression, clinicians use a structured questionnaire that includes routine baseline questions and follow-up questions to assess the interviewee's condition. This paper introduces HiQuE (Hierarchical Question Embedding network), a novel depression detection framework that leverages the hierarchical relationship between primary and follow-up questions in clinical interviews. HiQuE can effectively capture the importance of each question in diagnosing depression by learning mutual information across multiple modalities. We conduct extensive experiments on the widely-used clinical interview data, DAIC-WOZ, where our model outperforms other state-of-the-art multimodal depression detection models and emotion recognition models, showcasing its clinical utility in depression detection.