9th Annual Conference of the International Speech Communication Association

Brisbane, Australia
September 22-26, 2008

Exploiting the ASR N-Best by Tracking Multiple Dialog State Hypotheses

Jason D. Williams

AT&T Labs Research, USA

When the top ASR hypothesis is incorrect, often the correct hypothesis is listed as an alternative in the ASR N-Best list. Whereas traditional spoken dialog systems have struggled to exploit this information, this paper argues that a dialog model that tracks a distribution over multiple dialog states can improve dialog accuracy by making use of the entire N-Best list. The key element of the approach is a generative model of the N-Best list given the user's true hidden action. An evaluation on real dialog data verifies that dialog accuracy rates are improved by making use of the entire N-Best list.

Full Paper

Bibliographic reference.  Williams, Jason D. (2008): "Exploiting the ASR n-best by tracking multiple dialog state hypotheses", In INTERSPEECH-2008, 191-194.