Investigating Speech Recognition for Improving Predictive AAC

Jiban Adhikary, Robbie Watling, Crystal Fletcher, Alex Stanage, Keith Vertanen


Abstract
Making good letter or word predictions can help accelerate the communication of users of high-tech AAC devices. This is particularly important for real-time person-to-person conversations. We investigate whether per forming speech recognition on the speaking-side of a conversation can improve language model based predictions. We compare the accuracy of three plausible microphone deployment options and the accuracy of two commercial speech recognition engines (Google and IBM Watson). We found that despite recognition word error rates of 7-16%, our ensemble of N-gram and recurrent neural network language models made predictions nearly as good as when they used the reference transcripts.
Anthology ID:
W19-1706
Volume:
Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Heidi Christensen, Kristy Hollingshead, Emily Prud’hommeaux, Frank Rudzicz, Keith Vertanen
Venue:
SLPAT
SIG:
SIGSLPAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
37–43
Language:
URL:
https://aclanthology.org/W19-1706
DOI:
10.18653/v1/W19-1706
Bibkey:
Cite (ACL):
Jiban Adhikary, Robbie Watling, Crystal Fletcher, Alex Stanage, and Keith Vertanen. 2019. Investigating Speech Recognition for Improving Predictive AAC. In Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies, pages 37–43, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Investigating Speech Recognition for Improving Predictive AAC (Adhikary et al., SLPAT 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-1706.pdf