Any-to-any voice conversion (VC) aims to convert the timbre of utterances
from and to any speakers seen or unseen during training. Various any-to-any VC
approaches have been proposed like AUTOVC, AdaINVC, and FragmentVC. AUTOVC, and
AdaINVC utilize source and target encoders to disentangle the content and
speaker information of the features. FragmentVC utilizes two encoders to encode
source and target information and adopts cross attention to align the source
and target features with similar phonetic content. Moreover, pre-trained
features are adopted. AUTOVC used dvector to extract speaker information, and
self-supervised learning (SSL) features like wav2vec 2.0 is used in FragmentVC
to extract the phonetic content information. Different from previous works, we
proposed S2VC that utilizes Self-Supervised features as both source and target
features for VC model. Supervised phoneme posteriororgram (PPG), which is
believed to be speaker-independent and widely used in VC to extract content
information, is chosen as a strong baseline for SSL features. The objective
evaluation and subjective evaluation both show models taking SSL feature CPC as
both source and target features outperforms that taking PPG as source feature,
suggesting that SSL features have great potential in improving VC.

本文介绍了几种任意语音转换方法（例如 AUTOVC、AdaINVC 和 FragmentVC）以及一种新方法 S2VC，该方法利用自监督学习（SSL）特征作为源和目标特征进行 VC 模型，该方法的客观评价和主观评价都显示出优于以 PPG 为源特征的模型，表明 SSL 特征在改进 VC 方面具有巨大潜力。