A probabilistic approach to gestural recognition and dialogue management.

Persistent Link:
http://hdl.handle.net/10150/187253
Title:
A probabilistic approach to gestural recognition and dialogue management.
Author:
Newell, Gary L.
Issue Date:
1995
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
This dissertation presents a new probabilistic approach to the handling of gestural recognition. The new Bayesian model (INCA) is introduced and various aspects of its performance are examined. This model is a meta-algorithmic approach and allows a variety of different gestural recognition techniques to be combined in such a way as to supplement one another's capabilities to produce better overall recognition rates. Results of testing on this model indicate that it can reduce system design time and can provide effective recognition algorithms in a relatively short design period. The dissertation also examines a new model for handling dialogue management in gestural interfaces. This model, Probabilistic Finite State Machines differs from traditional approaches to dialogue management in that it views the non-deterministic nature of gestural recognition as a normal aspect of the dialogue between user and system. Unlike traditional approaches, this model treats recognition errors as a natural, expected part of any dialogue and is designed to address such errors in a natural way.
Type:
text; Dissertation-Reproduction (electronic)
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Computer Science; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Bailey, Mary L.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleA probabilistic approach to gestural recognition and dialogue management.en_US
dc.creatorNewell, Gary L.en_US
dc.contributor.authorNewell, Gary L.en_US
dc.date.issued1995en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThis dissertation presents a new probabilistic approach to the handling of gestural recognition. The new Bayesian model (INCA) is introduced and various aspects of its performance are examined. This model is a meta-algorithmic approach and allows a variety of different gestural recognition techniques to be combined in such a way as to supplement one another's capabilities to produce better overall recognition rates. Results of testing on this model indicate that it can reduce system design time and can provide effective recognition algorithms in a relatively short design period. The dissertation also examines a new model for handling dialogue management in gestural interfaces. This model, Probabilistic Finite State Machines differs from traditional approaches to dialogue management in that it views the non-deterministic nature of gestural recognition as a normal aspect of the dialogue between user and system. Unlike traditional approaches, this model treats recognition errors as a natural, expected part of any dialogue and is designed to address such errors in a natural way.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairBailey, Mary L.en_US
dc.contributor.committeememberHudson, Scotten_US
dc.contributor.committeememberMahaney, Steveen_US
dc.contributor.committeememberSchlichting, Richarden_US
dc.identifier.proquest9603703en_US
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