Semi-Automatic Assessment of Vocalization Quality for Children With and Without Angelman Syndrome

Automated methods for processing of daylong audio recordings are efficient and may be an effective way of assessing developmental stage for typically developing children; however, their utility for children with developmental disabilities may be limited by constraints of algorithms and the scope of variables produced. Here, we present a novel utterance-level processing (ULP) system that 1) extracts utterances from daylong recordings, 2) verifies automated speaker tags using human annotation, and 3) provides vocal maturity metrics unavailable through automated systems. Study 1 examines the reliability and validity of this system in low-risk controls (LRC); Study 2 extends the ULP to children with Angelman syndrome (AS). Results showed that ULP annotations demonstrated high coder agreement across groups. Further, ULP metrics aligned with language assessments for LRC but not AS, perhaps reflecting limitations of language assessments in AS. We argue that ULP increases accuracy, efficiency, and accessibility of detailed vocal analysis for syndromic populations.