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A Mathematical Theory for Learning Semantic Languages by Abstract Learners
Using density evolution analysis, we demonstrate the emergence of learned skills when the ratio of the size of training texts to the number of skills exceeds a certain threshold.
An Attribute-Aligned Strategy for Learning Speech Representation
Specifically, we propose a layered-representation variational autoencoder (LR-VAE), which factorizes speech representation into attribute-sensitive nodes, to derive an identity-free representation for speech emotion recognition (SER), and an emotionless representation for speaker verification (SV).
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