In this paper, the use of the probabilities produced by a K-nearest neighbours (K-nn) estimator as confidence measure is investigated in an hypothesis verification post-processing scheme. The objective is to classify as correct or incorrect the outputs of a Gaussian mixture model (GMM) /HMM speech recognition system. Four confidence measures based on the Knn probability estimator are introduced. Preliminary experiments are reported and discussed on the TIMIT database.
Cite as: Lefèvre, F. (2000) Confidence measures based on the k-nn probability estimator. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 4, 195-197
@inproceedings{lefevre00_icslp,
author={Fabrice Lefèvre},
title={{Confidence measures based on the k-nn probability estimator}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 4, 195-197}
}