Unsupervised speech representation learning using WaveNet autoencoders

Aäron van den Oord
Aäron van den Oord

IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), pp. 2041-2053, 2019.

Cited by: 97|Bibtex|Views127|DOI:https://doi.org/10.1109/TASLP.2019.2938863
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Other Links: dblp.uni-trier.de|academic.microsoft.com|dl.acm.org|arxiv.org

Abstract:

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 t...More

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