Investigation of machine-learning-based stimuli for the remediation of children’s speech errors

Abstract

Children with speech sound disorders (SSDs) demonstrate difficulty producing phonemes correctly and may exhibit poor speech perception compared to their age-matched peers; however, group differences in speech perception skills remain largely unexplained. Developmental models of speech production posit that children’s ability to discriminate correct and incorrect productions in their own speech may be critical for developing accurate speech production. Historically, when children regularly mispronounce a phoneme, it has been essentially impossible to assess whether they perceive correct versus errors productions in their own speech, thus creating a clinical conundrum. How can we assess a child’s ability to perceive the accuracy of their own phoneme production when they cannot produce a correct production? Recent technological developments allow for acoustic alteration of children’s speech that digitally corrects speech sound errors while preserving natural characteristics of the child’s voice. This machine-learning-based stimuli may then be used as training and feedback tokens for remediation when treating children with speech sound disorders. Such acoustic alteration is possible within an accessible, user-friendly environment that is clinically feasible for speech-language pathologists with little acoustic training. Thus, the purpose of this study is to evaluate the acoustic and perceptual accuracy of acoustically-altered child-speech compared to natural speech tokens.