S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations
This work addresses voice conversion for applications like speech synthesis, but it is incremental as it builds on existing methods by integrating self-supervised features.
The paper tackles the problem of any-to-any voice conversion by proposing S2VC, a framework that uses self-supervised pretrained features for both source and target inputs, and it shows that this approach outperforms a baseline using phoneme posteriorgrams in objective and subjective evaluations.
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.