Learning Latent Representations for Speech Generation and Transformation

13 Apr 2017Wei-Ning HsuYu ZhangJames Glass

An ability to model a generative process and learn a latent representation for speech in an unsupervised fashion will be crucial to process vast quantities of unlabelled speech data. Recently, deep probabilistic generative models such as Variational Autoencoders (VAEs) have achieved tremendous success in modeling natural images... (read more)

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