Multiple Instance Learning for Inference of Child Attachment From Paralinguistic Aspects of Speech

Buker, A. , Alsofyani, H. A. O. and Vinciarelli, A. (2023) Multiple Instance Learning for Inference of Child Attachment From Paralinguistic Aspects of Speech. In: INTERSPEECH 2023, Dublin, Ireland, 20-24 Aug 2023, (doi: 10.21437/interspeech.2023-1200)

Abstract

Attachment is a psychological construct that accounts for the way children perceive their relationship with their caregivers. Depending on the attachment condition, a child can either be secure or insecure. Identifying as many insecure children as possible is important to mitigate the negative consequences of insecure attachment in adult life. For this reason, this article proposes an attachment recognition approach that, compared to other approaches, increases the Recall, the percentage of insecure children identified as such. The approach is based on Multiple Instance Learning, a body of methodologies dealing with data represented as "bags" of feature vectors. This is suitable for speech recordings because these are typically represented as vector sequences. The experiments involved 104 participants of age 5 to 9. The results show that insecure children can be identified with Recall up to 63.3% (accuracy up to 75%), an improvement with respect to most existing models.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Alsofyani, Huda Abdulgani O and Buker, Areej and Vinciarelli, Professor Alessandro
• Authors: Buker, A., Alsofyani, H. A. O., and Vinciarelli, A.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Computing Science
• Journal Name: INTERSPEECH 2023
• Publisher: ISCA