IEEE J Biomed Health Inform. 2023 Nov;27(11):5576-5587. doi: 10.1109/JBHI.2023.3304369. Epub 2023 Nov 7.
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.
依恋风格与心理健康和身体健康有显著关联。具体来说，不安全的依恋会导致个体更容易患上精神障碍和慢性疾病。本研究旨在开发一种依恋识别模型，能够从语音记录中区分安全和不安全的依恋风格，探索声学特征的重要性，同时评估性别差异。共有 199 名参与者使用基于网络的询问系统录制了他们对四个旨在触发其依恋系统的开放性问题的回答。通过处理这些录音来获取标准声学特征集 eGeMAPS，并应用递归特征消除来选择相关特征。使用性别相关和性别无关的方法训练了不同的监督机器学习模型来识别依恋风格。性别无关模型的测试准确率为 58.88%，而女性和男性的性别相关模型的测试准确率分别为 63.88%和 83.63%，这表明性别对依恋风格识别有很大影响，需要在进一步的研究中分别考虑。这些结果还表明，声学特征在远程评估依恋风格方面具有潜力，可以快速、客观地识别这种健康风险因素，从而支持大规模移动筛查系统的实施。
