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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/323226 |
| Date | January 2014 |
| Creators | Noriega Atala, Enrique |
| Contributors | Cohen, Paul R., Morrison, Clayton T., Morrison, Clayton T., Cohen, Paul R., Hartman, John H. |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Electronic Thesis |
| 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. |
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