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Dissertation/Thesis Abstract

An evaluation framework for adaptive user interfaces
by Noriega Atala, Enrique, M.S., The University of Arizona, 2014, 46; 1559719
Abstract (Summary)

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.

Indexing (document details)
Advisor: Cohen, Paul R., Morrison, Clayton T.
Commitee: Cohen, Paul R., Hartman, John H., Morrison, Clayton T.
School: The University of Arizona
Department: Computer Science
School Location: United States -- Arizona
Source: MAI 53/02M(E), Masters Abstracts International
Subjects: Artificial intelligence
Keywords: Adaptive user interfaces, Automatic speech recognition, Expected utility, Machine learning, Rational user interfaces
Publication Number: 1559719
ISBN: 978-1-321-00665-0
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