Keith Vertanen


2021

pdf bib
Accelerating Text Communication via Abbreviated Sentence Input
Jiban Adhikary | Jamie Berger | Keith Vertanen
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Typing every character in a text message may require more time or effort than strictly necessary. Skipping spaces or other characters may be able to speed input and reduce a user’s physical input effort. This can be particularly important for people with motor impairments. In a large crowdsourced study, we found workers frequently abbreviated text by omitting mid-word vowels. We designed a recognizer optimized for expanding noisy abbreviated input where users often omit spaces and mid-word vowels. We show using neural language models for selecting conversational-style training text and for rescoring the recognizer’s n-best sentences improved accuracy. On noisy touchscreen data collected from hundreds of users, we found accurate abbreviated input was possible even if a third of characters was omitted. Finally, in a study where users had to dwell for a second on each key, sentence abbreviated input was competitive with a conventional keyboard with word predictions. After practice, users wrote abbreviated sentences at 9.6 words-per-minute versus word input at 9.9 words-per-minute.

2019

pdf bib
Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies
Heidi Christensen | Kristy Hollingshead | Emily Prud’hommeaux | Frank Rudzicz | Keith Vertanen
Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies

pdf bib
Investigating Speech Recognition for Improving Predictive AAC
Jiban Adhikary | Robbie Watling | Crystal Fletcher | Alex Stanage | Keith Vertanen
Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies

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.

2012

pdf bib
Applying Prediction Techniques to Phoneme-based AAC Systems
Ha Trinh | Annalu Waller | Keith Vertanen | Per Ola Kristensson | Vicki L. Hanson
Proceedings of the Third Workshop on Speech and Language Processing for Assistive Technologies

2011

pdf bib
The Imagination of Crowds: Conversational AAC Language Modeling using Crowdsourcing and Large Data Sources
Keith Vertanen | Per Ola Kristensson
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing