  Non-parallel many-to-many voice conversion remains an interesting but
challenging speech processing task. Recently, AutoVC, a conditional autoencoder
based method, achieved excellent conversion results by disentangling the
speaker identity and the speech content using information-constraining
bottlenecks. However, due to the pure autoencoder training method, it is
difficult to evaluate the separation effect of content and speaker identity. In
this paper, a novel voice conversion framework, named $\boldsymbol T$ext
$\boldsymbol G$uided $\boldsymbol A$utoVC(TGAVC), is proposed to more
effectively separate content and timbre from speech, where an expected content
embedding produced based on the text transcriptions is designed to guide the
extraction of voice content. In addition, the adversarial training is applied
to eliminate the speaker identity information in the estimated content
embedding extracted from speech. Under the guidance of the expected content
embedding and the adversarial training, the content encoder is trained to
extract speaker-independent content embedding from speech. Experiments on
AIShell-3 dataset show that the proposed model outperforms AutoVC in terms of
naturalness and similarity of converted speech.
