Improving P300 spelling rate using language models and predictive spelling
نویسندگان
چکیده
منابع مشابه
Improving Native Language Identification by Using Spelling Errors
In this paper, we explore spelling errors as a source of information for detecting the native language of a writer, a previously under-explored area. We note that character n-grams from misspelled words are very indicative of the native language of the author. In combination with other lexical features, spelling error features lead to 1.2% improvement in accuracy on classifying texts in the TOE...
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This study compared a conventional P300 speller brain-computer interface (BCI) to one used in conjunction with a predictive spelling program. Performance differences in accuracy, bit rate, selections per minute, and output characters per minute (OCM) were examined. An 8×9 matrix of letters, numbers, and other keyboard commands was used. Participants (n = 24) were required to correctly complete ...
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Background: Spelling is characterized as a basic skill for the children’s writing literacy. A wide range of factors may contribute to the formation and/ or intensification of problems related to teaching writing competencies, so that spellings come with a serious challenge called “invented spelling”. It is also a major concern for teachers and parents of children with visual impairments. Theref...
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This paper describes a spelling correction system that functions as part of an intelligent tutor that carries on a natural language dialogue with its users. The process that searches the lexicon is adaptive as is the system filter, to speed up the process. The basis of our approach is the interaction between the parser and the spelling corrector. Alternative correction targets are fed back to t...
متن کاملCSE 256 ( Spring 2004 ) “ Language Models for Spelling Correction ”
This project examines the use of language models in a spelling correction system that adopts the “Noisy Channel Model”. Various models based on bigram counts are tested in an experiment where typos are introduced into a test corpus, and corrections are made by language model ranking alone. Simple bigram models perform noticeably better than the unigram model (84% accuracy vs. 74%). And more sop...
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ژورنال
عنوان ژورنال: Brain-Computer Interfaces
سال: 2017
ISSN: 2326-263X,2326-2621
DOI: 10.1080/2326263x.2017.1410418