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Introducing a Novel Community-Based Assessment Tool: The Computerized Social Affective Language Task (C-SALT)
Objectives: Assess the feasibility of using C-SALT, a low-cost computer program that children can operate independently, to gather vocalization data as part of a community-based social communication and motor battery.
Methods: C-SALT was administered to 67 children (mean age=10.6 years, 77% male) enrolled in summer camps for children with disabilities, or general YMCA programs. Thirty-seven participants had ASD according to parent report, 18 were typically developing controls, and 12 had non-ASD clinical diagnoses or first-degree relatives with ASD. C-SALT was the last task in a 20-minute mobile battery.
Results: Despite being the final task in the battery, C-SALT data was successfully collected from 80% of participants. Of the participants that did not complete C-SALT, 65% had autism and 50% had parent-reported speech-language impairments (mean age: 11.29 years). In response to this finding, we developed C-SALT-PL, containing paradigms modified to suit the needs of pre-literate or minimally verbal participants. Using largely automated methods (e.g., time stamps built into C-SALT output for each child), we have begun segmenting and analyzing facial expressions, gestures, and audio data collected via C-SALT. Although these analyses are preliminary, we expect that participant vocalizations will diverge in two primary areas that affect social communication: acoustic properties of voice (pitch variation, volume control, shimmer and jitter), and word choice (word frequency, lexical diversity, social/nonsocial focus). Our measure of sustained phonation, in particular, holds promise as a language-agnostic measure of vocal-motor control.
Conclusions: This project capitalizes on a natural synergy between computational linguistics and developmental psychopathology to precisely quantify real-world social communication difficulties in children with ASD. We will have C-SALT and C-SALT-PL available and prepared to demonstrate at IMFAR 2017, along with pilot data demonstrating the ability of these measures to distinguish diagnostic groups and quantify social communicative ability with high precision.