Bayesian fusion of confidence measures for speech recognition

@article{Kim2005BayesianFO,
  title={Bayesian fusion of confidence measures for speech recognition},
  author={Taeyoon Kim and Hanseok Ko},
  journal={IEEE Signal Processing Letters},
  year={2005},
  volume={12},
  pages={871-874}
}
The application of Bayesian fusion of confidence measures to speech recognition is proposed. Feature level, decision level, and hybrid fusion are considered under the Bayesian framework. The use of speaker-adapted feature-level Bayesian fusion reduced the error rate by 19.4% as compared to the conventional single feature-based confidence scoring in an isolated word out-of-vocabulary rejection test. The decision-level Bayesian fusion also showed better performance than the majority rule. Finally… CONTINUE READING

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  • The use of speaker-adapted feature-level Bayesian fusion reduced the error rate by 19.4% as compared to the conventional single feature-based confidence scoring in an isolated word out-of-vocabulary rejection test.
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