Sentence-level multi-modal feature learning for depression recognition Article Swipe
Guanghua Zhang
,
Guangping Zhuo
,
Yang Yang
,
Augix Guohua Xu
,
Shukui Ma
,
Hao Liu
,
Zhiyan Ren
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyt.2025.1439577
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyt.2025.1439577
Background The global prevalence of depression has escalated, exacerbated by societal and economic pressures. Current diagnostic methodologies predominantly utilize single-modality data, which, despite the existence of certain multi-modal strategies, often fail to effectively harness the distinct contributions of each modality in depression detection. Methods This study collected multi-modal features from 100 participants (67 depressed patients and 33 non-depressed individuals) to formulate a MMD2023 dataset, and introduces the Sentence-level Multi-modal Feature Learning (SMFL) approach, an automated system designed to enhance depression recognition. SMFL analyzes synchronized sentence-level segments of facial expressions, vocal features, and transcribed texts obtained from patient-doctor interactions. It incorporates Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM) networks to meticulously extract features from each modality, aligned with the structured temporal flow of dialogues. Additionally, the novel Cross-Modal Joint Attention (CMJAT) mechanism is developed to reconcile variances in feature representation across modalities, adeptly adjusting the influence of each modality and amplifying weaker signals to equate with more pronounced features. Results Validated on our collected MMD2023 dataset and a public available DAIC-WOZ containing 192 patients dataset, the SMFL achieves accuracies of 91% and 89% respectively, demonstrating superior performance in binary depression classification. This advanced approach not only achieves a higher precision in identifying depression but also ensures a balanced and unified multi-modal feature representation. Conclusion The SMFL methodology represents a significant advancement in the diagnostic processes of depression, promising a cost-effective, private, and accessible diagnostic tool that aligns with the PHQ-8 clinical standard. By broadening the accessibility of mental health resources, this methodology has the potential to revolutionize the landscape of psychiatric evaluation, augmenting the precision of depression identification and enhancing the overall mental health management infrastructure.
Related Topics
Concepts
Modality (human–computer interaction)
Feature (linguistics)
Modal
Modalities
Computer science
Depression (economics)
Sentence
Feature learning
Artificial intelligence
Representation (politics)
Convolutional neural network
Machine learning
Linguistics
Political science
Sociology
Macroeconomics
Polymer chemistry
Social science
Chemistry
Economics
Law
Politics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpsyt.2025.1439577
- OA Status
- gold
- Cited By
- 1
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408717571
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408717571Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fpsyt.2025.1439577Digital Object Identifier
- Title
-
Sentence-level multi-modal feature learning for depression recognitionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-03-21Full publication date if available
- Authors
-
Guanghua Zhang, Guangping Zhuo, Yang Yang, Augix Guohua Xu, Shukui Ma, Hao Liu, Zhiyan RenList of authors in order
- Landing page
-
https://doi.org/10.3389/fpsyt.2025.1439577Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fpsyt.2025.1439577Direct OA link when available
- Concepts
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Modality (human–computer interaction), Feature (linguistics), Modal, Modalities, Computer science, Depression (economics), Sentence, Feature learning, Artificial intelligence, Representation (politics), Convolutional neural network, Machine learning, Linguistics, Political science, Sociology, Macroeconomics, Polymer chemistry, Social science, Chemistry, Economics, Law, Politics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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