Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Towards High-fidelity Singing Voice Conversion with Acoustic Reference
and Contrastive Predictive Coding
summary: Recently, phonetic posteriorgrams (PPGs) based methods have been quite
popular in non-parallel singing voice conversion systems. However, due to the
lack of acoustic information in PPGs, style and naturalness of the converted
singing voices are still limited. To solve these problems, in this paper, we
utilize an acoustic reference encoder to implicitly model singing
characteristics. We experiment with different auxiliary features, including mel
spectrograms, HuBERT, and the middle hidden feature (PPG-Mid) of pretrained
automatic speech recognition (ASR) model, as the input of the reference
encoder, and finally find the HuBERT feature is the best choice. In addition,
we use contrastive predictive coding (CPC) module to further smooth the voices
by predicting future observations in latent space. Experiments show that,
compared with the baseline models, our proposed model can significantly improve
the naturalness of converted singing voices and the similarity with the target
singer. Moreover, our proposed model can also make the speakers with just
speech data sing.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Coding
summary: Recently, phonetic posteriorgrams (PPGs) based methods have been quite popular in non-parallel singing voice conversion systems. However, due to the lack of acoustic information in PPGs, style and naturalness of the converted singing voices are still limited. To solve these problems, in this paper, we utilize an acoustic reference encoder to implicitly model singing characteristics. We experiment with different auxiliary features, including mel spectrograms, HuBERT, and the middle hidden feature (PPG-Mid) of pretrained automatic speech recognition (ASR) model, as the input of the reference encoder, and finally find the HuBERT feature is the best choice. In addition, we use contrastive predictive coding (CPC) module to further smooth the voices by predicting future observations in latent space. Experiments show that, compared with the baseline models, our proposed model can significantly improve the naturalness of converted singing voices and the similarity with the target singer. Moreover, our proposed model can also make the speakers with just speech data sing.
id: http://arxiv.org/abs/2110.04754v1
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