Evaluating Automatic Speech Recognition for Child Speech Therapy Applications

Automatic speech recognition (ASR) technology can be a useful tool in mobile apps for child speech therapy, empowering children to complete their practice with limited caregiver supervision. However, little is known about the feasibility of performing ASR on mobile devices, particularly when training data is limited. In this study, we investigated the performance of two low-resource ASR systems on disordered speech from children. We compared the open-source PocketSphinx (PS) recognizer using adapted acoustic models and a custom template-matching (TM) recognizer. TM and the adapted models significantly out-perform the default PS model. On average, maximum likelihood linear regression and maximum a posteriori adaptation increased PS accuracy from 59.4% to 63.8% and 80.0%, respectively, suggesting that the models successfully captured speaker-specific word production variations. TM reached a mean accuracy of 75.8%.