Word level confidence annotation using combinations of features”, European conference on speech communication and technology

Word level confidence annotation using combinations of features”, European conference on speech communication and technology (2001)

by Rong Zhang , Er I. Rudnicky

Citations:

20 - 1 self

Datum	Value	Source
TITLE	Word level confidence annotation using combinations of features”, European conference on speech communication and technology	INFERENCE
AUTHOR NAME	Rong Zhang	SVM HeaderParse 0.2
AUTHOR AFFIL	School of Computer Science, Carnegie Mellon University	SVM HeaderParse 0.2
AUTHOR ADDR	5000 Forbes Avenue, Pittsburgh, PA 15213	SVM HeaderParse 0.2
AUTHOR NAME	Er I. Rudnicky	SVM HeaderParse 0.2
AUTHOR AFFIL	School of Computer Science, Carnegie Mellon University	SVM HeaderParse 0.2
AUTHOR ADDR	5000 Forbes Avenue, Pittsburgh, PA 15213	SVM HeaderParse 0.2
ABSTRACT	This paper describes the development of a word-level confidence metric suitable for use in a dialog system. Two aspects of the problems are investigated: the identification of useful features and the selection of an effective classifier. We find that two parse-level features, Parsing-Mode and Slot-Backoff-Mode, provide annotation accuracy comparable to that observed for decoder-level features. However, both decoderlevel and parse-level features independently contribute to confidence annotation accuracy. In comparing different classification techniques, we found that Support Vector Machines (SVMs) appear to provide the best accuracy. Overall we achieve 39.7 % reduction in annotation uncertainty for a binary confidence decision in a travel-planning domain. 1.	SVM HeaderParse 0.2
YEAR	2001	INFERENCE
PAGES	2105--2108	INFERENCE
CITATIONS	15 found	ParsCit 1.0