An Improved Combined-Dictionary Method for the Judgment of Voiced and Unvoiced Sounds

2020 International Conference on Mathematics and Computers in Science and Engineering (MACISE)(2020)

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摘要
To solve the problem of the confusion in atoms between voiced and unvoiced dictionaries, an improved Combined-Dictionary method of the judgment in voiced and unvoiced sounds is proposed. Combining the correlation coefficient method and Mahalanobis distance, a new approach is explored to analyze the similarity of atoms. By analyzing the correlation in corresponding atoms, the appropriate threshold value is selected and the clean combined-dictionaries are obtained. The results have indicted, under the same conditions, the improved combined-dictionary method has higher accuracy and faster computing speed than the original method. 1
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关键词
Mahalanobis distance,correlation coefficient method,sparse representation,dictionary learning
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