Unsupervised speech representation learning using WaveNet autoencoders

25 Jan 2019Jan ChorowskiRon J. WeissSamy BengioAäron van den Oord

We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms. The goal is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being invariant to confounding low level details in the signal such as the underlying pitch contour or background noise... (read more)

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