A handsome set of metrics to measure utterance classification performance in spoken dialog systems

Authors:
David Suendermann;Jackson Liscombe;Krishna Dayanidhi;Roberto Pieraccini
Affiliations:
SpeechCycle Labs, New York;SpeechCycle Labs, New York;SpeechCycle Labs, New York;SpeechCycle Labs, New York
Venue:
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Year:
2009

Citing 3
Cited 1

How may I help you?

Speech Communication - Special issue on interactive voice technology for telecommunication applications (IVITA '96)
Multi-slot semantics for natural-language call routing systems

NAACL-HLT-Dialog '07 Proceedings of the Workshop on Bridging the Gap: Academic and Industrial Research in Dialog Technologies
From rule-based to statistical grammars: Continuous improvement of large-scale spoken dialog systems

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing

Validation of a dialog system for language learners

SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a set of metrics describing classification performance for individual contexts of a spoken dialog system as well as for the entire system. We show how these metrics can be used to train and tune system components and how they are related to Caller Experience, a subjective measure describing how well a caller was treated by the dialog system.