Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Voice Conversion Using Sequence-to-Sequence Learning of Context
Posterior Probabilities
summary: Voice conversion (VC) using sequence-to-sequence learning of context
posterior probabilities is proposed. Conventional VC using shared context
posterior probabilities predicts target speech parameters from the context
posterior probabilities estimated from the source speech parameters. Although
conventional VC can be built from non-parallel data, it is difficult to convert
speaker individuality such as phonetic property and speaking rate contained in
the posterior probabilities because the source posterior probabilities are
directly used for predicting target speech parameters. In this work, we assume
that the training data partly include parallel speech data and propose
sequence-to-sequence learning between the source and target posterior
probabilities. The conversion models perform non-linear and variable-length
transformation from the source probability sequence to the target one. Further,
we propose a joint training algorithm for the modules. In contrast to
conventional VC, which separately trains the speech recognition that estimates
posterior probabilities and the speech synthesis that predicts target speech
parameters, our proposed method jointly trains these modules along with the
proposed probability conversion modules. Experimental results demonstrate that
our approach outperforms the conventional VC.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities
summary: Voice conversion (VC) using sequence-to-sequence learning of context posterior probabilities is proposed. Conventional VC using shared context posterior probabilities predicts target speech parameters from the context posterior probabilities estimated from the source speech parameters. Although conventional VC can be built from non-parallel data, it is difficult to convert speaker individuality such as phonetic property and speaking rate contained in the posterior probabilities because the source posterior probabilities are directly used for predicting target speech parameters. In this work, we assume that the training data partly include parallel speech data and propose sequence-to-sequence learning between the source and target posterior probabilities. The conversion models perform non-linear and variable-length transformation from the source probability sequence to the target one. Further, we propose a joint training algorithm for the modules. In contrast to conventional VC, which separately trains the speech recognition that estimates posterior probabilities and the speech synthesis that predicts target speech parameters, our proposed method jointly trains these modules along with the proposed probability conversion modules. Experimental results demonstrate that our approach outperforms the conventional VC.
id: http://arxiv.org/abs/1704.02360v4
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