Applying prediction techniques to phoneme-based AAC systems

Authors:
Keith Vertanen;Ha Trinh;Annalu Waller;Vicki L. Hanson;Per Ola Kristensson
Affiliations:
Montana Tech of the University of Montana;University of Dundee;University of Dundee;University of Dundee;University of St Andrews
Venue:
SLPAT '12 Proceedings of the Third Workshop on Speech and Language Processing for Assistive Technologies
Year:
2012

Citing 7
Cited 1

Semantic knowledge in word completion

Proceedings of the 7th international ACM SIGACCESS conference on Computers and accessibility
Topic modeling in fringe word prediction for AAC

Proceedings of the 11th international conference on Intelligent user interfaces
Text prediction systems: a survey

Universal Access in the Information Society
Joint-sequence models for grapheme-to-phoneme conversion

Speech Communication
User Interaction with Word Prediction: The Effects of Prediction Quality

ACM Transactions on Accessible Computing (TACCESS)
Intelligent selection of language model training data

ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
The imagination of crowds: conversational AAC language modeling using crowdsourcing and large data sources

EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing

iSCAN: a phoneme-based predictive communication aid for nonspeaking individuals

Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is well documented that people with severe speech and physical impairments (SSPI) often experience literacy difficulties, which hinder them from effectively using orthographic-based AAC systems for communication. To address this problem, phoneme-based AAC systems have been proposed, which enable users to access a set of spoken phonemes and combine phonemes into speech output. In this paper we investigate how prediction techniques can be applied to improve user performance of such systems. We have developed a phoneme-based prediction system, which supports single phoneme prediction and phoneme-based word prediction using statistical language models generated using a crowdsourced AAC-like corpus. We incorporated our prediction system into a hypothetical 12-key reduced phoneme keyboard. A computational experiment showed that our prediction system led to 56.3% average keystroke savings.