Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Robust Training of Vector Quantized Bottleneck Models
summary: In this paper we demonstrate methods for reliable and efficient training of
discrete representation using Vector-Quantized Variational Auto-Encoder models
(VQ-VAEs). Discrete latent variable models have been shown to learn nontrivial
representations of speech, applicable to unsupervised voice conversion and
reaching state-of-the-art performance on unit discovery tasks. For unsupervised
representation learning, they became viable alternatives to continuous latent
variable models such as the Variational Auto-Encoder (VAE). However, training
deep discrete variable models is challenging, due to the inherent
non-differentiability of the discretization operation. In this paper we focus
on VQ-VAE, a state-of-the-art discrete bottleneck model shown to perform on par
with its continuous counterparts. It quantizes encoder outputs with on-line
$k$-means clustering. We show that the codebook learning can suffer from poor
initialization and non-stationarity of clustered encoder outputs. We
demonstrate that these can be successfully overcome by increasing the learning
rate for the codebook and periodic date-dependent codeword re-initialization.
As a result, we achieve more robust training across different tasks, and
significantly increase the usage of latent codewords even for large codebooks.
This has practical benefit, for instance, in unsupervised representation
learning, where large codebooks may lead to disentanglement of latent
representations.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Robust Training of Vector Quantized Bottleneck Models
summary: In this paper we demonstrate methods for reliable and efficient training of discrete representation using Vector-Quantized Variational Auto-Encoder models (VQ-VAEs). Discrete latent variable models have been shown to learn nontrivial representations of speech, applicable to unsupervised voice conversion and reaching state-of-the-art performance on unit discovery tasks. For unsupervised representation learning, they became viable alternatives to continuous latent variable models such as the Variational Auto-Encoder (VAE). However, training deep discrete variable models is challenging, due to the inherent non-differentiability of the discretization operation. In this paper we focus on VQ-VAE, a state-of-the-art discrete bottleneck model shown to perform on par with its continuous counterparts. It quantizes encoder outputs with on-line $k$-means clustering. We show that the codebook learning can suffer from poor initialization and non-stationarity of clustered encoder outputs. We demonstrate that these can be successfully overcome by increasing the learning rate for the codebook and periodic date-dependent codeword re-initialization. As a result, we achieve more robust training across different tasks, and significantly increase the usage of latent codewords even for large codebooks. This has practical benefit, for instance, in unsupervised representation learning, where large codebooks may lead to disentanglement of latent representations.
id: http://arxiv.org/abs/2005.08520v1
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