Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A maximum entropy approach to natural language processing
Computational Linguistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Using continuous features in the maximum entropy model
Pattern Recognition Letters
IEEE Transactions on Audio, Speech, and Language Processing
Computer Speech and Language
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Speech recognition errors are essentially unavoidable under the severe conditions of real fields, and so confidence estimation, which scores the reliability of a recognition result, plays a critical role in the development of speech recognition based real-field application systems. However, if we are to develop an application system that provides a high-quality service, in addition to achieving accurate confidence estimation, we also need to extract and exploit further supplementary information from a speech recognition engine. As a first step in this direction, in this paper, we propose a method for estimating the confidence of a recognition result while jointly detecting the causes of recognition errors based on a discriminative model. The confidence of a recognition result and the nonexistence/existence of error causes are naturally correlated. By directly capturing these correlations between the confidence and error causes, the proposed method enhances its estimation performance for the confidence and each error cause complementarily. In the initial speech recognition experiments, the proposed method provided higher confidence estimation accuracy than a discriminative model based state-of-the-art confidence estimation method. Moreover, the effective estimation mechanism of the proposed method was confirmed by the detailed analyses.