Automated anaysis of syllable complexity in children as an indicator of speech disorder

Speech disorders affecting intelligibility commonly occur in young children. One way to differentiate normal versus delayed speech development is to measure the ability to articulate increasingly complex syllables. We present a computer-assisted approach, Syllabic Cluster Analysis (SCA) as an objective measure of syllabic complexity. SCA uses clusters of acoustic landmarks to detect articulatory complexity in the production of syllables. Although most research using landmarks focuses on the lexical content of speech, SCA focuses on non-lexical differences which is well suited for analysis of speech with decreased intelligibility. Feasibility of this system to predict disordered speaker group is tested. Words recorded by normal adult (n = 10) and typical child (n = 20) speakers and children with speech disorders (n = 10) were ranked using a published word complexity measure to establish a high complexity word list (n= 20). Multinomial logistic regression models are fit for Landmarks per Syllabic Cluster (L...