"So...my child..." - How Child ADHD Influences theWay Parents Talk

被引:0
|
作者
Spiesberger, Anika A. [1 ]
Triantafyllopoulos, Andreas [1 ]
Kathan, Alexander [2 ]
Semertzidou, Anastasia [1 ]
Gawrilow, Caterina [3 ,7 ,8 ]
Reinelt, Tilman [4 ,7 ]
Rauch, Wolfgang A. [5 ]
Schuller, Bjorn W. [1 ,2 ,6 ]
机构
[1] Tech Univ Munich, CHI Chair Hlth Informat, MRI, Munich, Germany
[2] Univ Augsburg, Chair Embedded Intelligence Hlth Care & Wellbeing, Augsburg, Germany
[3] Univ Tubingen, Dept Psychol, Tubingen, Germany
[4] Univ Zurich, Dept Neonatol, Zurich, Switzerland
[5] Ludwigsburg Univ Educ, Dept Special Educ, Ludwigsburg, Germany
[6] Imperial Coll London, GLAM Grp Language Audio & Mus, London, England
[7] IDeA Ctr Res Individual Dev & Adapt Educ Children, Frankfurt, Germany
[8] DZPG German Ctr Mental Hlth, Mannheim, Germany
来源
INTERSPEECH 2024 | 2024年
关键词
attention-deficit/hyperactivity disorder; parent-child relationship; expressed emotion; computer audition; computational paralinguistics; linguistics; EXPRESSED EMOTION; SYMPTOMS; SPEECH; PRESCHOOL; DISORDER; MOTHERS;
D O I
10.21437/Interspeech.2024-744
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Attention-deficit/hyperactivity disorder (ADHD) exerts a psychological burden not only on affected individuals but also on their social support systems. Of particular interest are the parents, who often face challenges related to their child's condition, including its impact on their own mental well-being. The interaction among the child's symptomatology, parental mental health, and the parent-child relationship is a crucial area of investigation. Expressed Emotion (EE), as assessed through the Preschool Five Minute Speech Sample (PFMSS), serves as a valuable measure. However, manual annotation of EE can be cumbersome and impractical for continuous monitoring. To address this, we propose leveraging machine learning methods. This study presents an initial exploration into predicting children's ADHD diagnosis using linguistic and paralinguistic features derived from the PFMSS. Despite achieving a UAR score of 67.1 %, our results have not surpassed the performance of manually annotated EE.
引用
收藏
页码:2010 / 2014
页数:5
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