Nowadays, recognition-synthesis-based methods have been quite popular with
voice conversion (VC). By introducing linguistics features with good
disentangling characters extracted from an automatic speech recognition (ASR)
model, the VC performance achieved considerable breakthroughs. Recently,
self-supervised learning (SSL) methods trained with a large-scale unannotated
speech corpus have been applied to downstream tasks focusing on the content
information, which is suitable for VC tasks. However, a huge amount of speaker
information in SSL representations degrades timbre similarity and the quality
of converted speech significantly. To address this problem, we proposed a
high-similarity any-to-one voice conversion method with the input of SSL
representations. We incorporated adversarial training mechanisms in the
synthesis module using external unannotated corpora. Two auxiliary
discriminators were trained to distinguish whether a sequence of
mel-spectrograms has been converted by the acoustic model and whether a
sequence of content embeddings contains speaker information from external
corpora. Experimental results show that our proposed method achieves comparable
similarity and higher naturalness than the supervised method, which needs a
huge amount of annotated corpora for training and is applicable to improve
similarity for VC methods with other SSL representations as input.

提出了一种基于声音转换技术的高相似度任意语音转换方法，该方法中使用了自监督学习方法及其提取的 SSL 输入表示，采用对抗训练机制和辅助判别器，通过外部未标注的大型语音语料库，在合成模块中成功解决了与 SSL 表示中的说话者信息与音色相似度有关的限制问题。实验结果表明，我们的方法在自然度高且相似度可比的情况下比监督方法更加灵活，同时可以推广至其他 SSL 表示作为输入的 VC 方法，而且无需大量标注数据的支持。