Persistent Link:
http://hdl.handle.net/10150/323226
Title:
An Evaluation Framework for Adaptive User Interface
Author:
Noriega Atala, Enrique
Issue Date:
2014
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:
With the rise of powerful mobile devices and the broad availability of computing power, Automatic Speech Recognition is becoming ubiquitous. A flawless ASR system is still far from existence. Because of this, interactive applications that make use of ASR technology not always recognize speech perfectly, when not, the user must be engaged to repair the transcriptions. We explore a rational user interface that uses of machine learning models to make its best effort in presenting the best repair strategy available to reduce the time in spent the interaction between the user and the system as much as possible. A study is conducted to determine how different candidate policies perform and results are analyzed. After the analysis, the methodology is generalized in terms of a decision theoretical framework that can be used to evaluate the performance of other rational user interfaces that try to optimize an expected cost or utility.
Type:
text; Electronic Thesis
Keywords:
automatic speech recognition; expected utility; machine learning; rational user interfaces; Computer Science; adaptive user interfaces
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Computer Science
Degree Grantor:
University of Arizona
Advisor:
Cohen, Paul R.; Morrison, Clayton T.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleAn Evaluation Framework for Adaptive User Interfaceen_US
dc.creatorNoriega Atala, Enriqueen_US
dc.contributor.authorNoriega Atala, Enriqueen_US
dc.date.issued2014-
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.abstractWith the rise of powerful mobile devices and the broad availability of computing power, Automatic Speech Recognition is becoming ubiquitous. A flawless ASR system is still far from existence. Because of this, interactive applications that make use of ASR technology not always recognize speech perfectly, when not, the user must be engaged to repair the transcriptions. We explore a rational user interface that uses of machine learning models to make its best effort in presenting the best repair strategy available to reduce the time in spent the interaction between the user and the system as much as possible. A study is conducted to determine how different candidate policies perform and results are analyzed. After the analysis, the methodology is generalized in terms of a decision theoretical framework that can be used to evaluate the performance of other rational user interfaces that try to optimize an expected cost or utility.en_US
dc.typetexten
dc.typeElectronic Thesisen
dc.subjectautomatic speech recognitionen_US
dc.subjectexpected utilityen_US
dc.subjectmachine learningen_US
dc.subjectrational user interfacesen_US
dc.subjectComputer Scienceen_US
dc.subjectadaptive user interfacesen_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorCohen, Paul R.en_US
dc.contributor.advisorMorrison, Clayton T.en_US
dc.contributor.committeememberMorrison, Clayton T.en_US
dc.contributor.committeememberCohen, Paul R.en_US
dc.contributor.committeememberHartman, John H.en_US
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