mlsanityData to 7 Oct 2026

Climate and Weather: Inspecting Depression Detection via Emotion Recognition

arXiv 2204.14099 ↗First posted 29 Apr 2022cs.CLcited by 8

Not on the map. Why?

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 29 Apr 2022.

Builds on

10 of 27 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Multimodal Transformer for Unaligned Multimodal Language Sequencescited by 637
  3. 2017A Structured Self-attentive Sentence Embeddingcited by 318
  4. 2019Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysiscited by 82
  5. 2020Emotion recognition by fusing time synchronous and time asynchronous representationscited by 26
  6. 2018Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselinescited by 23
  7. 2019Speech Emotion Recognition Using Multi-hop Attention Mechanismcited by 18
  8. 2018Improved TDNNs using Deep Kernels and Frequency Dependent Grid-RNNscited by 6
  9. 2019Speaker diarisation using 2D self-attentive combination of embeddingscited by 4
  10. 2019Towards Robust Deep Neural Networks for Affect and Depression Recognition from Speechcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 4; 2025: 1; 2026: 1.

8 papers.

  1. 2024EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmarkcited by 39
  2. 2022Symptom Identification for Interpretable Detection of Multiple Mental Disorderscited by 20
  3. 2023Self-supervised representations in speech-based depression detectioncited by 18
  4. 2024When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detectioncited by 16
  5. 2025Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generationcited by 3
  6. 2024Confidence Estimation for Automatic Detection of Depression and Alzheimer's Disease Based on Clinical Interviewscited by 2
  7. 2024Investigating Acoustic-Textual Emotional Inconsistency Information for Automatic Depression Detectioncited by 1
  8. 2026Dynamic Summary Generation for Interpretable Multimodal Depression Detectioncited by 1