Paper: Automatic Detection Of Poor Speech Recognition At The Dialogue Level

ACL ID P99-1040
Title Automatic Detection Of Poor Speech Recognition At The Dialogue Level
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1999
Authors

The dialogue strategies used by a spoken dialogue system strongly influence performance and user sat- isfaction. An ideal system would not use a single fixed strategy, but would adapt to the circumstances at hand. To do so, a system must be able to identify dialogue properties that suggest adaptation. This paper focuses on identifying situations where the speech recognizer is performing poorly. We adopt a machine learning approach to learn rules from a dialogue corpus for identifying these situations. Our results show a significant improvement over the baseline and illustrate that both lower-level acoustic features and higher-level dialogue features can af- fect the performance of the learning algorithm.