Using automatic speech recognition to identify pediatric speech errors

Abstract

Speech delay is a childhood language problem that might resolve without intervention, but might alternatively presage continued speech and language deficits. Thus, early detection through screening might help to identify children for whom intervention is warranted. The goal of this work is to develop Automatic Speech Recognition (ASR) methods to partially automate screening for speech delay in young children. Speech data were recorded from typically developing and speech delayed children (N = 63) aged 6 to 9 years old during administration of the Goldman Fristoe Test of Articulation (GFTA). Monophone Hidden Markov Model (HMM) acoustic models were trained on speech data obtained from 207 typically developing children in the same age range. These training data consisted of a total of about 18,000 single-word utterances. The HMMs were then used to develop an utterance verification system to distinguish correct versus error productions. Several variations of the recognition strategy, feature extraction, and scoring methods were investigated. The best overall ASR result for distinguishing normal versus abnormal speech is approximately 86%. It is hypothesized that the ASR methods could approach the level of accuracy of speech therapists for this task (agreement among multiple therapists is over 95%), but a much larger database may be needed.