Description: Wavelet Subband coding for speaker recognition
The fn will calculated subband energes as given in the att tech paper of ruhi sarikaya and others. the fn also calculates the DCT part. using this fn and other algo for pattern classification(VQ,GMM) speaker identification could be achived. the progress in extraction is also indicated by progress bar.-Wavelet Subband coding for speaker recognitionThe fn will calculated subband energes as given in the att tech paper of ruhi sarikaya and others. The fn also calculates the DCT part. Using this fn and other algo for pattern classification (VQ, GMM) speaker identification could be achived. the progress in extraction is also indicated by progress bar. Platform: |
Size: 88064 |
Author:chan man man |
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Description: automatic Speaker recognition system using Gmm and Mf-automatic Speaker recognition system using Gmm and Mfcc Platform: |
Size: 576512 |
Author:vikram |
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Description: Speaker recognition is the task of validating individual s identity using invariant features extracted from their voices print. Speaker recognition technology common applications include authentication, surveillance and forensic applications. This Paper investigates the performance of three automatic model selections based on Gaussian Mixture Model (GMM). These approaches are Bayesian information criterion (BIC), Bayesian Ying–Yang harmony empirical learning criterion (BYY-HEC) and Bayesian Ying–Yang harmony data smoothing learning criterion (BYY-HDS). Experimental evaluation of these methods is presented. Platform: |
Size: 243712 |
Author:ZCEEE |
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Description: 使用voicebox进行说话人识别,初步介绍如何用GMM（Gaussian Mixture Model）的方法来进行说话人辨识（Speaker Identification），并在Matlab下，尝试通过调整参数来提高得分-For speaker recognition using the voicebox, initially describes how to use GMM (Gaussian Mixture Model) approach to speaker recognition (Speaker Identification), and in Matlab, try to increase the score by adjusting the parameters Platform: |
Size: 9216 |
Author:戴平平 |
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Description: The number of states in GMM as the generative model of the frames is obtained using
k-means algorithm. This also helps to initialize the mean vector and the covariance
matrix of the individual state of the GMM. The training LPC frames collected from
three speech segments are subjected to PCA for dimensionality reduction and are
subjected to k-means algorithm. The total number of frames is equal to the total
number of vectors that are subjected to k-means clustering. Platform: |
Size: 728064 |
Author:Khan17
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Search - speaker recognition in gmm using Matlab