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• W1601277738 abstract "In the paper, the method of acoustic model complexi ty level selection for automatic speech recognition is proposed. Selection of the appropriate model comple xity affects significantly the accuracy of speech r ecognition. For this reason the selection of the appropriate comple xity level is crucial for practical speech recognit ion applications, where end user effort related to the implementation of speech recognition system is important. We inve stigated the correlation between speech recognition accuracy and two popular information criteria used in statistic al model evaluation: Bayesian Information Criterion and Akai ke Information Criterion computed for applied acous tic models. Experiments carried out for language models related to general medicine texts and radiology diagnostic reporting in CT and MR showed strong correlation of speech recognition accuracy and BIC criterion. Using this depende ncy, the procedure of Gaussian mixture count selection for a coustic model was proposed. Application of this pro cedure makes it possible to create the acoustic model maximizing th e speech recognition accuracy without additional co mputational costs related to alternative cross-validation appro ach and without reduction of training set size, whi ch is unavoidable in the case of cross-validation approach." @default.
• W1601277738 created "2016-06-24" @default.
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• W1601277738 date "2011-01-01" @default.
• W1601277738 modified "2023-10-18" @default.
• W1601277738 title "Optimal acoustic model complexity selection in polish medical speech recognition" @default.
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