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Evaluation of Methods to Combine Different Speech Recognizers
2015
Proceedings of the 2015 Federated Conference on Computer Science and Information Systems
The paper deals with the problem of improving speech recognition by combining outputs of several different recognizers. We are presenting our results obtained by experimenting with different classification methods which are suitable to combine outputs of different speech recognizers. Methods which were evaluated are: k-Nearest neighbors (KNN), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Logistic Regression (LR) and maximum likelihood (ML). Results showed, that
doi:10.15439/2015f62
dblp:conf/fedcsis/RasymasR15
fatcat:6co7tgemm5fbtb43xpg5ksz3vy