Acoustic characterization of developmental speech disorders

Abstract

A novel approach to classifying children with developmental speech delays (DSD) involving /r/ was developed. The approach first derives an acoustic classification of /r/ tokens based on their forced Viterbi alignment to a five-state hidden Markov model (HMM) of normally articulated /r/. Children with DSD are then classified in terms of the proportion of their /r/ productions that fall into each broad acoustic class. This approach was evaluated using 953 examples of /r/ as produced by 18 DSD children and an approximately equal number of /r/ tokens produced by a much larger number of normally articulating children. The acoustic classification identified three broad categories of /r/ that differed substantially in how they aligned to the normal speech /r/ HMM. Additionally, these categories tended to partition tokens uttered by DSD children from those uttered by normally articulating children. Similarities among the DSD children and average normal child measured in terms of the proportion of their /r/ productions that fell into each of the three broad acoustic categories were used to perform a hierarchical clustering. This clustering revealed groupings of DSD children who tended to approach /r/ production in one of several acoustically distinct manners.