MFE-Former: Disentangling Emotion-Identity Dynamics via Self-Supervised Learning for Enhancing Speech-Driven Depression Detection
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces MFE-Former, a novel speech analysis method for detecting depression by analyzing emotional changes. MFE-Former effectively distinguishes depression by identifying distinct emotional expression patterns in individuals.
Area Of Science
- Computational linguistics
- Psychiatry
- Machine learning
Background
- Acoustic features in speech are vital for depression detection.
- Existing methods struggle with emotional variability and speaker identity interference.
- Accurate detection of emotional changes is crucial for understanding depression.
Purpose Of The Study
- To develop a robust speech-based depression detection method addressing emotional variability.
- To introduce the MFE-Former model, combining self-supervised and supervised learning.
- To enhance the extraction of depression-related emotional patterns from speech.
Main Methods
- Developed the Emotional Word Reading Experiment (EWRE) dataset.
- Generated fine-grained emotional representations using context similarity and orthogonality constraints.
- Employed a Transformer decoder to reconstruct spectral structures and decouple identity.
- Integrated multi-scale emotion change perception and Bernoulli distribution-based decision modules.
Main Results
- MFE-Former successfully differentiates emotional expression patterns between depressed patients and healthy individuals.
- Patients with depression showed a higher tendency for negative emotional expression.
- Healthy individuals exhibited more positive emotional expression.
- Experimental results on EWRE and AVEC 2014 datasets demonstrated superior performance over state-of-the-art methods.
Conclusions
- MFE-Former effectively detects depression from speech by analyzing emotional changes, even with varying emotional patterns.
- The method's ability to decouple speaker identity enhances the sensitivity to emotional cues.
- MFE-Former offers a promising advancement in computational approaches to mental health assessment.
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