Diacritic-Level Pronunciation Analysis Using Phonological Features

Speech sound disorders affect 10% of preschool and school-age children, adversely affecting their communication, academic perfor- mance, and interaction level. Effective pronunciation training re- quires prolonged supervised practice and interaction. Unfortunately, many children have limited or no access to a speech-language pathol- ogist. 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 archi- tecture 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 be- tween the typically developing and the speech disordered groups.