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Semantic confidence measurement for spoken dialog systems

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4 Author(s)
R. Sarikaya ; Human Language Technol. Group, IBM T.J. Watson Res. Center, Yorktown Heights, NY, USA ; Yuqing Gao ; M. Picheny ; H. Erdogan

This paper proposes two methods to incorporate semantic information into word and concept level confidence measurement. The first method uses tag and extension probabilities obtained from a statistical classer and parser. The second method uses a maximum entropy based semantic structured language model to assign probabilities to each word. Incorporation of semantic features into a lattice posterior probability based confidence measure provides significant improvements compared to posterior probability when used together in an air travel reservation task. At 5% False Alarm (FA) rate relative improvements of 28% and 61% in Correct Acceptance (CA) rate are achieved for word level and concept level confidence measurements, respectively.

Published in:

IEEE Transactions on Speech and Audio Processing  (Volume:13 ,  Issue: 4 )