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This research investigates the use of utterance-level features for confidence scoring. Confidence scores are used to accept or reject user utterances in our conversational weather information system 10. We have developed an automatic labeling algorithm based on a semantic frame comparison between recognized and transcribed orthographies. We explore recognition-based features along with semantic, linguistic, and application-specific features for utterance rejection. Discriminant analysis is used in an iterative process to select the best set of classification features for our utterance rejection sub-system. Experiments show that we can correctly reject over 60% of incorrectly understood utterances while accepting 98% of all correctly understood utterances.
Pao et al. (Mon,) studied this question.
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