Integration of Continuous Speech Recognition and Information Retrieval for Mutually Optimal Performance (1999)
| Venue: | COMPUTER SCIENCE DEPARTMENT, CARNEGIE MELLON UNIVERSITY. HTTP://WWW.CS.CMU.EDU/~MSIEGLER/PUBLISH/PHD/THESIS.PS.GZ SINGHAL |
| Citations: | 15 - 1 self |
BibTeX
@TECHREPORT{Siegler99integrationof,
author = {Matthew A. Siegler},
title = {Integration of Continuous Speech Recognition and Information Retrieval for Mutually Optimal Performance},
institution = {COMPUTER SCIENCE DEPARTMENT, CARNEGIE MELLON UNIVERSITY. HTTP://WWW.CS.CMU.EDU/~MSIEGLER/PUBLISH/PHD/THESIS.PS.GZ SINGHAL},
year = {1999}
}
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Abstract
Traditionally, indexing and searching of speech content in multimedia databases have been achieved through a combination of separately constructed speech recognition and information retrieval engines. Although each technology has a legacy of research, only recently have efforts been made to study the potential suboptimality of this strategy, and none of these efforts specifically addresses the presence of uncertainty in automatically generated transcriptions. This research develops a refinement of the most common information retrieval relevance formula, TFIDF, to incorporate uncertainty as a retrieval feature, along with a set of techniques to acquire this uncertainty from multiple hypotheses produced by existing speech recognition data structures. In the process a greater amount of evidence is extracted than is available in the most likely transcription hypothesis, and overall retrieval precision and recall are improved. The term weighting scheme known as the inverse document frequenc...
