文件名称:Investigation_on_Model_Selection_Criteria_for_Spe
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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.
相关搜索: BIC
SPEAKER
bayesian
ying
yang
speaker
recognition
using
gmm
Gaussian
mixture
model
gmm
gmm
speaker
matlab
speaker
recognition
in
gmm
using
Matlab
SPEAKER
RECOGNITION
MATLAB
gmm
语音识别
SPEAKER
bayesian
ying
yang
speaker
recognition
using
gmm
Gaussian
mixture
model
gmm
gmm
speaker
matlab
speaker
recognition
in
gmm
using
Matlab
SPEAKER
RECOGNITION
MATLAB
gmm
语音识别
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Investigation_on_Model_Selection_Criteria_for_Speaker_Identification.pdf