Speech sound disorders affect 10% of preschool and school-age children, adversely affecting their communication, academic performance, and interaction level. Effective pronunciation training requires prolonged supervised practice and interaction. Unfortunately, many children have limited or no access to a speech-language pathologist. Computer-assisted pronunciation training has the potential for being a highly effective teaching aid; however, to-date such systems remain incapable of identifying pronunciation errors with sufficient accuracy. We propose a system that combines a multi-target architecture with weighted finite-state transducers to first segment and then analyze an utterance in terms of its phonological features. We analyze a corpus of 90 children aged 4-7 and find differences between the typically developing and the speech disordered groups.
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