  Voice cloning is a difficult task which requires robust and informative
features incorporated in a high quality TTS system in order to effectively copy
an unseen speaker's voice. In our work, we utilize features learned in a
self-supervised framework via the Bootstrap Your Own Latent (BYOL) method,
which is shown to produce high quality speech representations when specific
audio augmentations are applied to the vanilla algorithm. We further extend the
augmentations in the training procedure to aid the resulting features to
capture the speaker identity and to make them robust to noise and acoustic
conditions. The learned features are used as pre-trained utterance-level
embeddings and as inputs to a Non-Attentive Tacotron based architecture, aiming
to achieve multispeaker speech synthesis without utilizing additional speaker
features. This method enables us to train our model in an unlabeled
multispeaker dataset as well as use unseen speaker embeddings to copy a
speaker's voice. Subjective and objective evaluations are used to validate the
proposed model, as well as the robustness to the acoustic conditions of the
target utterance.
