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Leveraging the fact that speaker identity and content vary on different timescales, \acrlong{fhvae} (\acrshort{fhvae}) uses different latent variables tosymbolize these two attributes. Disentanglement of these attributes is carriedout by different prior settings of the corresponding latent variables. For theprior of speaker identity variable, \acrshort{fhvae} assumes it is a Gaussiandistribution with an utterance-scale varying mean and a fixed variance. Bysetting a small fixed variance, the training process promotes identityvariables within one utterance gathering close to the mean of their prior.However, this constraint is relatively weak, as the mean of the prior changesbetween utterances. Therefore, we introduce contrastive learning into the\acrshort{fhvae} framework, to make the speaker identity variables gatheringwhen representing the same speaker, while distancing themselves as far aspossible from those of other speakers. The model structure has not been changedin this work but only the training process, thus no additional cost is neededduring testing. Voice conversion has been chosen as the application in thispaper. Latent variable evaluations include speaker verification andidentification for the speaker identity variable, and speech recognition forthe content variable. Furthermore, assessments of voice conversion performanceare on the grounds of fake speech detection experiments. Results show that theproposed method improves both speaker identity and content feature extractioncompared to \acrshort{fhvae}, and has better performance than baseline onconversion.

Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder