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Published on: October 3, 2018
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Different in different ways: A network-analysis approach to voice and prosody in Autism Spectrum Disorder
Ethan Weed1, Riccardo Fusaroli1, Elizabeth Simmons2
1Linguistics, Cognitive Science, and Semiotics, Aarhus University, Aarhus, Denmark.
Summary
This study used network analysis to find distinct acoustic profiles in speech for individuals with autism spectrum disorder (ASD) and neurotypical (NT) speakers. The findings reveal new ways to understand speech patterns in ASD.
Area of Science:
- Linguistics
- Neuroscience
- Speech Science
Background:
- Distinguishing prosody between autism spectrum disorder (ASD) and neurotypical (NT) speakers is challenging.
- Atypical speech patterns in ASD may stem from diverse acoustic profiles rather than a single atypicality.
Purpose of the Study:
- To investigate if network-based acoustic profiles can explain group differences in prosody between ASD and NT speakers.
- To explore the utility of community-detection algorithms in identifying speaker clusters based on acoustic features.
Main Methods:
- Speech data from 26 speakers (with and without ASD) were modeled as networks of acoustic features.
- A community-detection algorithm identified acoustically similar speaker clusters.
- Clusters were compared with human atypicality ratings (naïve and expert).
Main Results:
- Three distinct speaker clusters emerged: primarily ASD, primarily NT, and mixed ASD/NT.
- Human raters reliably distinguished ASD from NT speakers across all clusters.
- Network analysis revealed underlying acoustic structures in speech patterns.
Conclusions:
- Community-detection methods offer a complementary approach to human ratings for analyzing prosody in ASD.
- Network analysis can help characterize diverse intonation profiles within the ASD population.
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