Improved Disentangled Speech Representations Using Contrastive Learning in Factorized Hierarchical Variational Autoencoder

Leveraging the fact that speaker identity and content vary on different time scales, factorized hierarchical variational autoencoder (FHVAE) uses different latent variables to symbolize these two attributes. Disentanglement of these attributes is carried out by different prior settings of the corresponding latent variables. For the prior of speaker identity variable, FHVAE assumes it is a Gaussian distribution with an utterance-scale varying mean and a fixed variance. By setting a small fixed variance, the training process promotes identity variables within one utterance gathering close to the mean of their prior. However, this constraint is relatively weak, as the mean of the prior changes between utterances. Therefore, we introduce contrastive learning into the FHVAE framework, to make the speaker identity variables gathering when representing the same speaker, while distancing themselves as far as possible from those of other speakers. The model structure has not been changed in this work but only the training process, thus no additional cost is needed during testing. Voice conversion has been chosen as the application in this paper. Latent variable evaluations include speaker verification and identification for the speaker identity variable, and speech recognition for the content variable. Furthermore, assessments of voice conversion performance are on the grounds of fake speech detection experiments. Results show that the proposed method improves both speaker identity and content feature extraction compared to FHVAE, and has better performance than baseline on conversion.