580 research outputs found
Neural Discrete Representation Learning
Learning useful representations without supervision remains a key challenge
in machine learning. In this paper, we propose a simple yet powerful generative
model that learns such discrete representations. Our model, the Vector
Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways:
the encoder network outputs discrete, rather than continuous, codes; and the
prior is learnt rather than static. In order to learn a discrete latent
representation, we incorporate ideas from vector quantisation (VQ). Using the
VQ method allows the model to circumvent issues of "posterior collapse" --
where the latents are ignored when they are paired with a powerful
autoregressive decoder -- typically observed in the VAE framework. Pairing
these representations with an autoregressive prior, the model can generate high
quality images, videos, and speech as well as doing high quality speaker
conversion and unsupervised learning of phonemes, providing further evidence of
the utility of the learnt representations
Nonparallel Emotional Speech Conversion
We propose a nonparallel data-driven emotional speech conversion method. It
enables the transfer of emotion-related characteristics of a speech signal
while preserving the speaker's identity and linguistic content. Most existing
approaches require parallel data and time alignment, which is not available in
most real applications. We achieve nonparallel training based on an
unsupervised style transfer technique, which learns a translation model between
two distributions instead of a deterministic one-to-one mapping between paired
examples. The conversion model consists of an encoder and a decoder for each
emotion domain. We assume that the speech signal can be decomposed into an
emotion-invariant content code and an emotion-related style code in latent
space. Emotion conversion is performed by extracting and recombining the
content code of the source speech and the style code of the target emotion. We
tested our method on a nonparallel corpora with four emotions. Both subjective
and objective evaluations show the effectiveness of our approach.Comment: Published in INTERSPEECH 2019, 5 pages, 6 figures. Simulation
available at http://www.jian-gao.org/emoga
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