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Related Experiment Video

Updated: Jul 19, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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An Online Attachment Style Recognition System Based on Voice and Machine Learning.

Lucia Gomez-Zaragoza, Javier Marin-Morales, Elena Parra Vargas

    IEEE Journal of Biomedical and Health Informatics
    |August 11, 2023
    PubMed
    Summary

    This study developed a voice-based model to identify attachment styles, finding that gender significantly impacts accuracy. Separate models for men and women show higher recognition rates for this mental health risk factor.

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    Area of Science:

    • Psychology
    • Computer Science
    • Health Informatics

    Background:

    • Insecure attachment styles are linked to increased risks of mental and physical health issues.
    • Objective assessment of attachment styles is crucial for early intervention and risk management.

    Purpose of the Study:

    • To develop a machine learning model for distinguishing secure from insecure attachment styles using voice recordings.
    • To investigate the role of acoustic features and gender differences in attachment style recognition.

    Main Methods:

    • 199 participants provided voice recordings in response to attachment-triggering questions.
    • Acoustic features were extracted using the eGeMAPS set, with recursive feature elimination for selection.
    • Supervised machine learning models were trained using gender-independent and gender-dependent approaches.

    Main Results:

    • The gender-independent model achieved 58.88% test accuracy.
    • Gender-dependent models showed higher accuracy: 63.88% for women and 83.63% for men.
    • Results highlight significant gender influence on voice-based attachment style recognition.

    Conclusions:

    • Acoustic properties hold potential for remote, objective assessment of attachment styles.
    • Considering gender separately improves model performance, suggesting tailored approaches for health risk identification.
    • This research supports developing large-scale mobile screening systems for attachment-related health risks.