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Speaker identification using probabilistic PCA model selection
Jen-Tzung Chien
, Chuan Wei Ting
Department of Electrical and Computer Engineering
Research output
:
Contribution to conference
›
Paper
›
peer-review
5
Scopus citations
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Dive into the research topics of 'Speaker identification using probabilistic PCA model selection'. Together they form a unique fingerprint.
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Keyphrases
Model Selection
100%
Speaker Identification
100%
Probabilistic PCA
100%
Probabilistic Principal Component Analysis
100%
PCA Model
100%
Covariance Matrix
50%
Principal Component Analysis Model
50%
Gaussian Mixture Model
33%
Popular
16%
Expectation-maximization Algorithm
16%
Well-defined
16%
Recognition Performance
16%
Probability Distribution
16%
Model Regularization
16%
Speaker Recognition
16%
Speech Features
16%
Mixture Components
16%
Selection Model
16%
Speech Mixtures
16%
Gaussian Function
16%
Factor Analysis
16%
Full Covariance
16%
Full Covariance Matrix
16%
Establish Model
16%
Feature Component
16%
Computer Science
Speaker Identification
100%
Component Analysis
100%
Principal Components
100%
Analysis Model
50%
Diagonal Covariance Matrix
50%
Gaussian Mixture Model
33%
Recognition Performance
16%
Covariance Matrix
16%
Regularization
16%
Speaker Recognition
16%
Mathematics
Model Selection
100%
Principal Component Analysis
100%
Covariance Matrix
57%
Analysis Model
42%
Diagonal
42%
Gaussian Mixture Model
28%
Regularization
14%
Gaussian Distribution
14%
Covariance
14%
Probability Distribution
14%
Expectation-Maximization Algorithm
14%
Factor Analysis
14%