We have recently been conducting research on developing spoken dialogue systems to provide conversational practice for a learner of a foreign language. One of the most critical aspects of such a system is speech recognition errors, since they often take the dialogue thread down a wrong turn that is very confusing to the student and may be irrecoverable. In this paper we report on a machine learning technique to assist the process of selection from a list of N-best candidates based on a high-level description of the semantics of the preceding dialogue. In a user simulation experiment, we show that a significant reduction in sentence error rate can be achieved, from 29.2% to 23.6%. We have not yet verified that our techniques hold for real user data.
Cite as: Wu, H.-C., Seneff, S. (2007) Reducing recognition error rate based on context relationships among dialogue turns. Proc. Interspeech 2007, 2701-2704, doi: 10.21437/Interspeech.2007-709
@inproceedings{wu07g_interspeech,
author={Hsu-Chih Wu and Stephanie Seneff},
title={{Reducing recognition error rate based on context relationships among dialogue turns}},
year=2007,
booktitle={Proc. Interspeech 2007},
pages={2701--2704},
doi={10.21437/Interspeech.2007-709}
}