Applying prediction techniques to phoneme-based AAC systems

Keith Vertanen, Ha Trinh, Annalu Waller, Vicki L. Hanson, Per Ola Kristensson

    Research output: Contribution to conferencePaperpeer-review

    8 Citations (Scopus)

    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.

    Conference

    Conference3rd Workshop on Speech and Language Processing for Assistive Technologies, SLPAT 2012 at the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2012
    Country/TerritoryCanada
    CityMontreal
    Period7/06/128/06/12

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