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Previous works in zero-shot text-to-speech (ZS-TTS) have attempted to enhanceits systems by enlarging the training data through crowd-sourcing or augmentingexisting speech data. However, the use of low-quality data has led to a declinein the overall system performance. To avoid such degradation, instead ofdirectly augmenting the input data, we propose a latent filling (LF) methodthat adopts simple but effective latent space data augmentation in the speakerembedding space of the ZS-TTS system. By incorporating a consistency loss, LFcan be seamlessly integrated into existing ZS-TTS systems without the need foradditional training stages. Experimental results show that LF significantlyimproves speaker similarity while preserving speech quality.

Latent Filling: Latent Space Data Augmentation for Zero-shot Speech Synthesis