Unsupervised speech representation learning using WaveNet autoencoders
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), pp. 2041-2053, 2019.
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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