STAR Analytical Services

Most children effortlessly learn how to coordinate movements for normal speech production. About one in twelve preschool-aged children, however, show delays in speech production capability that may put them at risk for academic and behavioral difficulties, if not identified and treated. Automated tools that can distinguish between children with and without speech and language impairments could serve as a useful clinical tool for early identification of speech related disorders in young children. In this study, we consider measures based on detecting sequences of acoustic landmarks characteristic of normal speech production over multisyllabic words and continuous speech samples. Ten normal adults, ten typical children and ten with speech disorders recorded twenty multisyllabic words and thirty-three sentences. Acoustic landmarks patterns within utterances and syllabic clusters are examined to characterize differences in landmark sequences between normal and disordered speech. Shannon’s Entropy and ROC analysis are used to evaluate the landmark patterns as potential diagnostic measures of a-typical speech production. We discuss these results and our future work toward developing a fully automated clinical screening tool.