An Online Attachment Style Recognition System Based on Voice and Machine Learning

Attachment styles are known to have significant associations with mental and physical health. Specifically, insecure attachment leads individuals to higher risk of suffering from mental disorders and chronic diseases. The aim of this study is to develop an attachment recognition model that can disti...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2023-11, Vol.PP (11), p.1-12
Main Authors: Gomez-Zaragoza, Lucia, Marin-Morales, Javier, Vargas, Elena Parra, Giglioli, Irene Alice Chicchi, Raya, Mariano Alcaniz
Format: Article
Language:English
Subjects:
Accuracy
Acoustic features
Acoustic properties
Acoustics
Analytical models
artificial intelligence
Attachment
Behavioral sciences
Biological system modeling
Feature extraction
Gender
Gender aspects
Health risks
Interrogation
Learning algorithms
Machine learning
Mental disorders
Model testing
psychometrics
Risk factors
Sex differences
speech analysis
Speech recognition
statistical machine learning
Supervised learning
voice
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Summary:Attachment styles are known to have significant associations with mental and physical health. Specifically, insecure attachment leads individuals to higher risk of suffering from mental disorders and chronic diseases. The aim of this study is to develop an attachment recognition model that can distinguish between secure and insecure attachment styles from voice recordings, exploring the importance of acoustic features while also evaluating gender differences. A total of 199 participants recorded their responses to four open questions intended to trigger their attachment system using a web-based interrogation system. The recordings were processed to obtain the standard acoustic feature set eGeMAPS, and recursive feature elimination was applied to select the relevant features. Different supervised machine learning models were trained to recognize attachment styles using both gender-dependent and gender-independent approaches. The gender-independent model achieved a test accuracy of 58.88%, whereas the gender-dependent models obtained 63.88% and 83.63% test accuracy for women and men respectively, indicating a strong influence of gender on attachment style recognition and the need to consider them separately in further studies. These results also demonstrate the potential of acoustic properties for remote assessment of attachment style, enabling fast and objective identification of this health risk factor, and thus supporting the implementation of large-scale mobile screening systems.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2023.3304369