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Related Experiment Video

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Automatically Predicting Perceived Conversation Quality in a Pediatric Sample Enriched for Autism.

Yahan Yang¹, Sunghye Cho¹, Maxine Covello²

¹University of Pennsylvania, Philadelphia, PA.

Interspeech

|July 26, 2024

Summary

This study shows that analyzing children's conversations can automatically predict social interaction quality. This technology could help monitor progress and identify targets for children with communication challenges.

Keywords:

autism spectrum disorder conversational audio analysis machine learning classification and interpretation

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Area of Science:

Computational linguistics
Developmental psychology
Speech-language pathology

Background:

Social interaction quality is crucial for child development.
Assessing social communication in children, particularly those with autism spectrum disorder (ASD), can be challenging.
Objective, scalable methods are needed to evaluate social interaction skills.

Purpose of the Study:

To develop and evaluate automated methods for predicting social interaction quality in children based on natural conversations.
To compare the performance of machine learning classifiers with human raters in assessing conversational quality.
To identify key features that contribute to predicting communication success.

Main Methods:

Collected natural conversations between children and unfamiliar adults.
Extracted hand-crafted acoustic and lexical features from the conversations.
Utilized machine learning classifiers to predict social interaction quality across six dimensions.
Employed a pretrained audio transformer to extract advanced acoustic features.

Main Results:

The best classifier achieved 61% accuracy in predicting conversational quality, outperforming human raters (49%).
A subset of acoustic and lexical features was identified as crucial for predicting communication quality.
Incorporating features from a pretrained audio transformer improved prediction accuracy to 68%.

Conclusions:

Automated prediction of conversation quality offers an inexpensive and objective approach for monitoring intervention progress in children.
This technology can aid in identifying specific intervention targets to enhance conversational success in children with communication challenges.
The findings support the potential of AI-driven tools in developmental and clinical assessments.