Investigating Speech Recognition for Improving Predictive AAC

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

SLPAT '19: Proceedings of the Workshop on Speech and Language Processing for Assistive Technologies, 2019.

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 performing speech recognition on the speakingside 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.