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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. Keywords: autism spectrum disorder, children, prosody, social communication, assessment, dyadic interaction, Human social interaction necessitates that each participant continually perceive, plan, and express multimodal pragmatic and affective cues. Thus, a person's ability to interact effectively may be compromised when there is [...]

Byline: Carlos Busso (1), Murtaza Bulut (1), Chi-Chun Lee (1), Abe Kazemzadeh (1), Emily Mower (1), Samuel Kim (1), Jeannette N. Chang (1), Sungbok Lee (1), Shrikanth S. Narayanan (1) Keywords: Audio-visual database; Dyadic interaction; Emotion; Emotional assessment; Motion capture system Abstract: Since emotions are expressed through a combination of verbal and non-verbal channels, a joint analysis of speech and gestures is required to understand expressive human communication. To facilitate such investigations, this paper describes a new corpus named the 'interactive emotional dyadic motion capture database' (IEMOCAP), collected by the Speech Analysis and Interpretation Laboratory (SAIL) at the University of Southern California (USC). This database was recorded from ten actors in dyadic sessions with markers on the face, head, and hands, which provide detailed information about their facial expressions and hand movements during scripted and spontaneous spoken communication scenarios. The actors performed selected emotional scripts and also improvised hypothetical scenarios designed to elicit specific types of emotions (happiness, anger, sadness, frustration and neutral state). The corpus contains approximately 12 h of data. The detailed motion capture information, the interactive setting to elicit authentic emotions, and the size of the database make this corpus a valuable addition to the existing databases in the community for the study and modeling of multimodal and expressive human communication. Author Affiliation: (1) Speech Analysis and Interpretation Laboratory (SAIL), University of Southern California, Los Angeles, CA, 90089, USA Article History: Registration Date: 12/09/2008 Received Date: 15/10/2007 Accepted Date: 02/09/2008 Online Date: 05/11/2008