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Bayesian Adversarial Learning for Speaker Recognition
Jen Tzung Chien
, Chun Lin Kuo
電機工程學系
研究成果
:
Conference contribution
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同行評審
5
引文 斯高帕斯（Scopus）
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Keyphrases
Generative Adversarial Networks
100%
Adversarial Learning
100%
Speaker Recognition
100%
I-vector
25%
Variational
25%
Network Applications
12%
Sampling Methods
12%
Computation Cost
12%
Encoder
12%
Assure
12%
Learning Efficiency
12%
Learning Objectives
12%
Low Quality Data
12%
Misclassified
12%
Probabilistic Linear Discriminant Analysis
12%
Variational Autoencoder
12%
Imbalanced Data
12%
Model Regularization
12%
Recognition-based
12%
Performance Efficiency
12%
Diverse Data
12%
Regularized Model
12%
Variational Inference
12%
Weight Uncertainty
12%
Gradient Value
12%
Artificial Data
12%
Marginalization
12%
Generation Performance
12%
Gradient Vanishing
12%
Wasserstein Distance
12%
Model Collapse
12%
Mode Collapse
12%
Collapse Problem
12%
Computer Science
Generative Adversarial Networks
100%
Speaker Recognition
100%
Discriminator
25%
Computation Cost
12%
Linear Discriminant Analysis
12%
Regularization
12%
Artificial Data
12%
Social Exclusion
12%
Variational Autoencoder
12%
Mathematics
Bayesian
100%
Regularization
33%
Real Data
33%
Discriminant Analysis
33%
Superiority
33%
Marginalization
33%
Wasserstein Distance
33%
Engineering
Speaker Recognition
100%
Discriminator
66%
Regularization
33%
Real Data
33%
Autoencoder
33%