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Updated: May 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Vector Quantization-Based Counterfactual Augmentation for Speech-Based Depression Detection Under Data Scarcity.

Lishi Zuo, Man-Wai Mak

    IEEE Journal of Biomedical and Health Informatics
    |May 2, 2025
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    Summary
    This summary is machine-generated.

    Data scarcity hinders depression detection. A new counterfactual augmentation (CF aug) framework generates features to improve speech-based depression detection, overcoming overfitting and bias issues effectively.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computational Linguistics

    Background:

    • Data scarcity is a significant challenge in developing accurate depression detection models.
    • Limited data often leads to overfitting and bias, compromising model performance.
    • Existing methods struggle to generalize effectively under data-scarce conditions.

    Purpose of the Study:

    • To introduce a novel counterfactual augmentation (CF aug) framework for speech-based depression detection.
    • To address the challenges of data scarcity, overfitting, and bias in depression detection models.
    • To enhance the robustness and generalizability of AI models in medical diagnosis.

    Main Methods:

    • Developed a counterfactual augmentation (CF aug) framework utilizing a deep network with a counterfactual layer.
    • Integrated a group-wise vector quantization module to explore feature vector impact on outcomes.
    • Generated latent features by transforming original data representations to their opposite class.

    Main Results:

    • The CF aug framework effectively alleviates overfitting and bias issues stemming from data scarcity.
    • Achieved competitive performance compared to state-of-the-art methods on two depression detection datasets.
    • Demonstrated the framework's potential for other medical diagnostic domains and modalities.

    Conclusions:

    • Counterfactual augmentation is a promising approach to mitigate data scarcity in AI-driven medical diagnosis.
    • The proposed CF aug framework enhances the reliability of depression detection models.
    • This method shows potential for broader applications in data-limited diagnostic scenarios.