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- W2380854439 abstract "This paper improves the traditional method of Gaussian Mixture Mode(lGMM) parameters initialization at the time of GMM training.A new approach which combines division and K-means clustering is presented.The experiment shows that the proposed method can achieve the average recognition rate increase by 15.47% and 7.5% compared with the randomization and Kmeans clustering.At the same time,the impact of the order of GMM,covariance threshold and pre-emphasis coefficient on system recognition rate are studied.Meanwhile,the experiment results are analyzed in detail.In order to make the speaker recognition system get a higher recognition rate,their optimal values are chosen from the experiment data.The experiment shows that the system can achieve the recognition rate with above 90% under the provided experimental condition." @default.
- W2380854439 created "2016-06-24" @default.
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- W2380854439 date "2010-01-01" @default.
- W2380854439 modified "2023-09-26" @default.
- W2380854439 title "Research on GMM text-independent speaker recognition" @default.
- W2380854439 hasPublicationYear "2010" @default.
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