Systematic design of spoken prompts
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interpreting symptoms of cognitive load in speech input
UM '99 Proceedings of the seventh international conference on User modeling
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Evaluating tutors that listen: an overview of project LISTEN
Smart machines in education
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Towards robust semantic role labeling
Computational Linguistics
Responding to Student Uncertainty in Spoken Tutorial Dialogue Systems
International Journal of Artificial Intelligence in Education
Advances in children's speech recognition within an interactive literacy tutor
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
ITSPOKE: an intelligent tutoring spoken dialogue system
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Generating Instruction Automatically for the Reading Strategy of Self-Questioning
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Understanding mental states in natural language
IWCS-8 '09 Proceedings of the Eighth International Conference on Computational Semantics
Marker-Passing inference in the scone knowledge-base system
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
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Free-form spoken input would be the easiest and most natural way for young children to communicate to an intelligent tutoring system. However, achieving such a capability poses a challenge both to instruction design and to automatic speech recognition. To address the difficulties of accepting such input, we adopt the framework of predictable response training, which aims at simultaneously achieving linguistic predictability and educational utility. We design instruction in this framework to teach children the reading comprehension strategy of self-questioning. To filter out some misrecognized speech, we combine acoustic confidence with language modeling techniques that exploit the predictability of the elicited responses. Compared to a baseline that does neither, this approach performs significantly better in concept recall 47% vs. 28% and precision 61% vs. 39% on 250 unseen utterances from 34 previously unseen speakers. We conclude with some design implications for future speech enabled tutoring systems.