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An Attribute-Aligned Strategy for Learning Speech Representation
[article]
2021
arXiv
pre-print
Advancement in speech technology has brought convenience to our life. However, the concern is on the rise as speech signal contains multiple personal attributes, which would lead to either sensitive information leakage or bias toward decision. In this work, we propose an attribute-aligned learning strategy to derive speech representation that can flexibly address these issues by attribute-selection mechanism. Specifically, we propose a layered-representation variational autoencoder (LR-VAE),
arXiv:2106.02810v1
fatcat:vrzlgjle2fe7lpfpddacm4xsfe