Application of the Karhunen-Loève Expansion to Feature Selection and Ordering
IEEE Transactions on Computers
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In this paper a technique for detection and rejection of incorrectly recognized words is described. The speech recognition system we used is based on a speaker-independent continuous density Hidden Markov Model recognizer and so-called mumble model, which structure and function is also depicted. An improved rejection technique is presented in comparison with the heuristic rejection method that we previously used. The new method is fully statistically based. Therefore selection of features for training and classification, procedures for statistical models parameters estimation, and experimental results are reported. The improved rejection technique achieves approximately 12% error rate in detection of incorrectly recognized words.