Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Creating New Voices using Normalizing Flows
summary: Creating realistic and natural-sounding synthetic speech remains a big
challenge for voice identities unseen during training. As there is growing
interest in synthesizing voices of new speakers, here we investigate the
ability of normalizing flows in text-to-speech (TTS) and voice conversion (VC)
modes to extrapolate from speakers observed during training to create unseen
speaker identities. Firstly, we create an approach for TTS and VC, and then we
comprehensively evaluate our methods and baselines in terms of intelligibility,
naturalness, speaker similarity, and ability to create new voices. We use both
objective and subjective metrics to benchmark our techniques on 2 evaluation
tasks: zero-shot and new voice speech synthesis. The goal of the former task is
to measure the precision of the conversion to an unseen voice. The goal of the
latter is to measure the ability to create new voices. Extensive evaluations
demonstrate that the proposed approach systematically allows to obtain
state-of-the-art performance in zero-shot speech synthesis and creates various
new voices, unobserved in the training set. We consider this work to be the
first attempt to synthesize new voices based on mel-spectrograms and
normalizing flows, along with a comprehensive analysis and comparison of the
TTS and VC modes.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Creating New Voices using Normalizing Flows
summary: Creating realistic and natural-sounding synthetic speech remains a big challenge for voice identities unseen during training. As there is growing interest in synthesizing voices of new speakers, here we investigate the ability of normalizing flows in text-to-speech (TTS) and voice conversion (VC) modes to extrapolate from speakers observed during training to create unseen speaker identities. Firstly, we create an approach for TTS and VC, and then we comprehensively evaluate our methods and baselines in terms of intelligibility, naturalness, speaker similarity, and ability to create new voices. We use both objective and subjective metrics to benchmark our techniques on 2 evaluation tasks: zero-shot and new voice speech synthesis. The goal of the former task is to measure the precision of the conversion to an unseen voice. The goal of the latter is to measure the ability to create new voices. Extensive evaluations demonstrate that the proposed approach systematically allows to obtain state-of-the-art performance in zero-shot speech synthesis and creates various new voices, unobserved in the training set. We consider this work to be the first attempt to synthesize new voices based on mel-spectrograms and normalizing flows, along with a comprehensive analysis and comparison of the TTS and VC modes.
id: http://arxiv.org/abs/2312.14569v1
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