An Ensemble Model for the Analysis of Parent-Child Interactions from Text and Audio.
International Conference on Machine Learning and Applications(2023)
摘要
The interaction between parents and their children is foundational for their social and emotional development and well-being. Among measures that assess parent-child interaction quality, the Parent-Child Interaction Teaching Scale (PCI-TS) is an effective and well-established assessment tool that measures the quality of parent-child interactions. One of the significant hurdles in PCI-TS is identifying parent-child behaviors early on to aid parents in addressing initial behavioral issues. However, manual evaluations are resource-intensive and time-consuming, and restrict the accessibility of such assessments. In this research, we present an ensemble model to categorize various behavior types in parent-child interaction scales such as PCI-TS. We have collected over 2,000 minutes of video of parents and their children emerging in specific tasks such as drawing or object manipulation. These videos have been labeled based on PCI-TS by trained healthcare professionals. Our ensemble model has been trained to recognize emotions and psychological elements within parent-child interactions. This model is adjusted by multiple modalities and aspects of these psychological elements. We posit that our study introduces the first model that employs a new framework to integrate numerous behavioral factors to recognize these psychological elements within parent-child interactions using audio and text modalities. Our suggested model can discern and detect the semantic and syntactic features of audio and language in parent-child interactions according to the PCI-TS scale. The results of our evaluation showed an enhancement in performance when implementing this strategy, compared to similar approaches by 16% in F1-score.
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