Title
New word-level and sentence-level confidence scoring using graph theory calculus and its evaluation on speech understanding
Abstract
A lot of work has been devoted to the estimation of confidence measures for speech recognizers. In the quite extended case where a word-graph speech recognizer is in use, we will present new confidence measures employing the graph theory that shows us how to estimate some interesting characteristics about the different paths through the graph that constitute the recognition solutions, without the need of expanding them all. We will take advantage of some of these features to generate confidence scores both at the word and sentence level. We will also compare this new confidence scoring to more traditional ones and will find similar behavior with less computational load and with an increase in the simplicity of the approach that will lead to more generalization power of the confidence estimation to different applications of the recognizer.
Year
Venue
Keywords
2005
INTERSPEECH
graph theory
Field
DocType
Citations 
Graph theory,Confidence measures,Graph,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Sentence
Conference
7
PageRank 
References 
Authors
0.60
3
7