Publications
2021 |
Hair, A.; Zhao, G.; Ahmed, B.; Ballard, K. J.; Gutierrez-Osuna, R. Assessing Posterior-Based Mispronunciation Detection on Field-Collected Recordings from Child Speech Therapy Sessions Proceedings Article In: Proc. Interspeech, 2021. Links | BibTeX | Tags: Automatic Speech Recognition, Childhood apraxia of speech, Speech @inproceedings{adam2021interspeech, |
2020 |
Hair, A; Markoulli, C; Monroe, P; McKechnie, J; Ballard, K J; Ahmed, B; Gutierrez-Osuna, R Preliminary Results From a Longitudinal Study of a Tablet-Based Speech Therapy Game Proceedings Article In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing, ACM, 2020, ISBN: 978-1-4503-6819-3/20/04. Abstract | Links | BibTeX | Tags: Automatic Speech Recognition, Childhood apraxia of speech, Health, Speech @inproceedings{hair2020chi, We previously developed a tablet-based speech therapy game called Apraxia World to address barriers to treatment and increase child motivation during therapy. In this study, we examined pronunciation improvements, child engagement over time, and caregiver evaluation performance while using our game. We recruited ten children to play Apraxia World at home during two four-week treatment blocks, separated by a two-week break; nine of ten have completed the protocol at time of writing. In the treatment blocks, children’s utterances were evaluated either by caregivers or an automated pronunciation framework. Preliminary analysis suggests that children made significant therapy gains with Apraxia World, even though caregivers evaluated pronunciation leniently. We also collected a corpus of child speech for offline examination. We will conduct additional analysis once all participants complete the protocol. |
2019 |
Hair, A; Ballard, K J; Ahmed, B; Gutierrez-Osuna, R Evaluating Automatic Speech Recognition for Child Speech Therapy Applications Proceedings Article In: ACM SIGACCESS Conference on Computers and Accessibility, ACM 2019, ISBN: 978-1-4503-6676-2/19/10. Abstract | Links | BibTeX | Tags: Automatic Speech Recognition, Childhood apraxia of speech, Health, Speech @inproceedings{hair2019evaluating, 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%. |