Christopher A. Pennington

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This paper describes progress toward a prototype implementation of a tool which aims to improve literacy in deaf high school and college students who are native (or near native) signers of American Sign Language (ASL). We envision a system that will take a piece of text written by a deaf student, analyze that text for grammatical errors, and engage that(More)
Word prediction systems can reduce the number of keystrokes required to form a message in a letter-based AAC system. It has been questioned, however, whether such savings translate into an enhanced communication rate due to the additional overhead (e.g., shifting of focus and repeated scanning of a prediction list) required in using such a system. Our(More)
Many people with severe speech and motor impairments make use of augmentative and alternative communication (AAC) systems. These systems can employ a variety of techniques to organize stored words, phrases, and sentences, and to make them available to the user. It is argued in this paper that an AAC system should make better use of the regularities in an(More)
We discuss a user model which can be tailored to different types of users in order to identify and correct English language errors. It is presented in the context of a written English tutoring system for deaf people who use American Sign Language. Our approach to identifying the errors is to augment a standard grammar of English with a set of error(More)
Individuals using an Augmentative and Alternative Communication (AAC) device communicate at less than 10% of the speed of “traditional” speech, creating a large communication gap. In this user study, we compare the communication rate of pseudo-impaired individuals using two different word prediction algorithms and a system without word prediction. Our(More)
Augmentative and Alternative Communication (AAC) is the eld of study concerned with providing devices and techniques to augment the communicative ability of a person whose disability makes it di cult to speak or otherwise communicate in an understandable fashion. For several years, we have been applying natural language processing techniques to the eld of(More)
People with severe speech and motor impairments (SSMI) can often use augmentative communication devices to help them communicate. While these devices can provide speech synthesis or text output, the rate of communication is typically very slow. Consequently, augmentative communication users often develop telegraphic patterns of language usage. A natural(More)
Word prediction systems can reduce the number of keystrokes required to form a message in a letter-based AAC system. It has been questioned however, whether such savings translate into an enhanced communication rate due to the additional cognitive load (e.g., shifting of focus and scanning of a prediction list) required in using such a system. Our(More)
In this paper we discuss how generation issues affect the design of a computer-assisted language learning tool designed to teach written English as a second language to deaf users of American Sign Language. We discuss a dual-component linguistic model that attempts to reflect the generation process of the learners. The first model component captures the(More)