# Climate and Weather: Inspecting Depression Detection via Emotion Recognition

arXiv: https://arxiv.org/abs/2204.14099 · HTML: https://mlsanity.com/paper/2204.14099/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 29 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis](https://mlsanity.com/paper/1911.05544/) (2019)
- [Emotion recognition by fusing time synchronous and time asynchronous representations](https://mlsanity.com/paper/2010.14102/) (2020)
- [Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines](https://mlsanity.com/paper/1803.07427/) (2018)
- [Speech Emotion Recognition Using Multi-hop Attention Mechanism](https://mlsanity.com/paper/1904.10788/) (2019)
- [Improved TDNNs using Deep Kernels and Frequency Dependent Grid-RNNs](https://mlsanity.com/paper/1802.06412/) (2018)
- [Speaker diarisation using 2D self-attentive combination of embeddings](https://mlsanity.com/paper/1902.03190/) (2019)
- [Towards Robust Deep Neural Networks for Affect and Depression Recognition from Speech](https://mlsanity.com/paper/1911.00310/) (2019)

## Built on by (most cited first, 8 of 8)

- [EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark](https://mlsanity.com/paper/2406.07162/) (2024)
- [Symptom Identification for Interpretable Detection of Multiple Mental Disorders](https://mlsanity.com/paper/2205.11308/) (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)
- [Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.01315/) (2025)
- [Confidence Estimation for Automatic Detection of Depression and Alzheimer's Disease Based on Clinical Interviews](https://mlsanity.com/paper/2407.19984/) (2024)
- [Investigating Acoustic-Textual Emotional Inconsistency Information for Automatic Depression Detection](https://mlsanity.com/paper/2412.18614/) (2024)
- [Dynamic Summary Generation for Interpretable Multimodal Depression Detection](https://mlsanity.com/paper/2604.11334/) (2026)

## Abstract (arXiv)

Automatic depression detection has attracted increasing amount of attention but remains a challenging task. Psychological research suggests that depressive mood is closely related with emotion expression and perception, which motivates the investigation of whether knowledge of emotion recognition can be transferred for depression detection. This paper uses pretrained features extracted from the emotion recognition model for depression detection, further fuses emotion modality with audio and text to form multimodal depression detection. The proposed emotion transfer improves depression detection performance on DAIC-WOZ as well as increases the training stability. The analysis of how the emotion expressed by depressed individuals is further perceived provides clues for further understanding of the relationship between depression and emotion.