Interpersonal synchrony across vocal and lexical modalities in interactions involving children with autism spectrum disorder

Author:

Lahiri Rimita1ORCID,Nasir Md2,Kumar Manoj3,Kim So Hyun4,Bishop Somer5,Lord Catherine6,Narayanan Shrikanth1

Affiliation:

1. Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, California 90089, USA

2. Microsoft Artificial Intelligence for Good Research Lab, Redmond, Washington 98052, USA

3. Amazon Alexa Artificial Intelligence, Cambridge, Massachusetts 02142, USA

4. Center for Autism and the Developing Brain, Weill Cornell Medicine, New York, New York 10065, USA

5. Department of Psychiatry, University of California, San Francisco, California 94143, USA

6. Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, California 90024, USA, , , , , ,

Abstract

Quantifying behavioral synchrony can inform clinical diagnosis, long-term monitoring, and individualised interventions in neuro-developmental disorders characterized by deficit in communication and social interaction, such as autism spectrum disorder. In this work, three different objective measures of interpersonal synchrony are evaluated across vocal and linguistic communication modalities. For vocal prosodic and spectral features, dynamic time warping distance and squared cosine distance of (feature-wise) complexity are used, and for lexical features, word mover's distance is applied to capture behavioral synchrony. It is shown that these interpersonal vocal and linguistic synchrony measures capture complementary information that helps in characterizing overall behavioral patterns.

Funder

Simons Foundation Autism Research Initiative

Foundation for the National Institutes of Health

Publisher

Acoustical Society of America (ASA)

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics

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