One year of contender: what have we learned about assessing and tuning industrial spoken dialog systems?

Authors:
David Suendermann;Roberto Pieraccini
Affiliations:
SpeechCycle, New York;ICSI, Berkeley
Venue:
SDCTD '12 NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data
Year:
2012

Citing 3
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Abstract

A lot. Since inception of Contender, a machine learning method tailored for computer-assisted decision making in industrial spoken dialog systems, it was rolled out in over 200 instances throughout our applications processing nearly 40 million calls. The net effect of this data-driven method is a significantly increased system performance gaining about 100,000 additional automated calls every month.