TINA: a natural language system for spoken language applications
Computational Linguistics
Using Natural Language Processing and discourse Features to Identify Understanding Errors
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Predicting automatic speech recognition performance using prosodic cues
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Confidence measures for dialogue management in the CU Communicator system
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
| Hi-index | 0.00 |
Spoken and multimodal dialogue systems typically make use of confidence scores to choose among (or reject) a speech recognizer's N-best hypotheses for a particular utterance. We argue that it is beneficial to instead choose among a list of candidate system responses. We propose a novel method in which a confidence score for each response is derived from a classifier trained on acoustic and lexical features emitted by the recognizer, as well as features culled from the generation of the candidate response itself. Our response-based method yields statistically significant improvements in F-measure over a baseline in which hypotheses are chosen based on recognition confidence scores only.