Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: NAUTILUS: a Versatile Voice Cloning System
summary: We introduce a novel speech synthesis system, called NAUTILUS, that can
generate speech with a target voice either from a text input or a reference
utterance of an arbitrary source speaker. By using a multi-speaker speech
corpus to train all requisite encoders and decoders in the initial training
stage, our system can clone unseen voices using untranscribed speech of target
speakers on the basis of the backpropagation algorithm. Moreover, depending on
the data circumstance of the target speaker, the cloning strategy can be
adjusted to take advantage of additional data and modify the behaviors of
text-to-speech (TTS) and/or voice conversion (VC) systems to accommodate the
situation. We test the performance of the proposed framework by using deep
convolution layers to model the encoders, decoders and WaveNet vocoder.
Evaluations show that it achieves comparable quality with state-of-the-art TTS
and VC systems when cloning with just five minutes of untranscribed speech.
Moreover, it is demonstrated that the proposed framework has the ability to
switch between TTS and VC with high speaker consistency, which will be useful
for many applications.
Thunk you very much for contribution!
Your judgement is refrected in arXivSearches.json, and is going to be used for VCLab's activity.
Thunk you so much.
Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: NAUTILUS: a Versatile Voice Cloning System
summary: We introduce a novel speech synthesis system, called NAUTILUS, that can generate speech with a target voice either from a text input or a reference utterance of an arbitrary source speaker. By using a multi-speaker speech corpus to train all requisite encoders and decoders in the initial training stage, our system can clone unseen voices using untranscribed speech of target speakers on the basis of the backpropagation algorithm. Moreover, depending on the data circumstance of the target speaker, the cloning strategy can be adjusted to take advantage of additional data and modify the behaviors of text-to-speech (TTS) and/or voice conversion (VC) systems to accommodate the situation. We test the performance of the proposed framework by using deep convolution layers to model the encoders, decoders and WaveNet vocoder. Evaluations show that it achieves comparable quality with state-of-the-art TTS and VC systems when cloning with just five minutes of untranscribed speech. Moreover, it is demonstrated that the proposed framework has the ability to switch between TTS and VC with high speaker consistency, which will be useful for many applications.
id: http://arxiv.org/abs/2005.11004v1
judge
Write [vclab::confirmed] or [vclab::excluded] in comment.