Reducing recognition error rate based on context relationships among dialogue turns
Hsu-Chih Wu, Stephanie Seneff · 2007
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. Index Terms: dialogue modeling, user simulation, confidence scoring, machine learning