Journal
JOURNAL OF SPEECH LANGUAGE AND HEARING RESEARCH
Volume 57, Issue 4, Pages 1162-1177Publisher
AMER SPEECH-LANGUAGE-HEARING ASSOC
DOI: 10.1044/2014_JSLHR-S-13-0062
Keywords
autism spectrum disorder; children; prosody; social communication; assessment; dyadic interaction
Funding
- National Science Foundation
- National Institutes of Health [PL-R21 HD065289]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1029373] Funding Source: National Science Foundation
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Purpose: The purpose of this study was to examine relationships between prosodic speech cues and autism spectrum disorder (ASD) severity, hypothesizing a mutually interactive relationship between the speech characteristics of the psychologist and the child. The authors objectively quantified acoustic-prosodic cues of the psychologist and of the child with ASD during spontaneous interaction, establishing a methodology for future large-sample analysis. Method: Speech acoustic-prosodic features were semiautomatically derived from segments of semistructured interviews (Autism Diagnostic Observation Schedule, ADOS; Lord, Rutter, DiLavore, & Risi, 1999; Lord et al., 2012) with 28 children who had previously been diagnosed with ASD. Prosody was quantified in terms of intonation, volume, rate, and voice quality. Research hypotheses were tested via correlation as well as hierarchical and predictive regression between ADOS severity and prosodic cues. Results: Automatically extracted speech features demonstrated prosodic characteristics of dyadic interactions. As rated ASD severity increased, both the psychologist and the child demonstrated effects for turn-end pitch slope, and both spoke with atypical voice quality. The psychologist's acoustic cues predicted the child's symptom severity better than did the child's acoustic cues. Conclusion: The psychologist, acting as evaluator and interlocutor, was shown to adjust his or her behavior in predictable ways based on the child's social-communicative impairments. The results support future study of speech prosody of both interaction partners during spontaneous conversation, while using automatic computational methods that allow for scalable analysis on much larger corpora.
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