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On account of growing demands for personalization, the need for a so-calledfew-shot TTS system that clones speakers with only a few data is emerging. Toaddress this issue, we propose Attentron, a few-shot TTS model that clonesvoices of speakers unseen during training. It introduces two special encoders,each serving different purposes. A fine-grained encoder extractsvariable-length style information via an attention mechanism, and acoarse-grained encoder greatly stabilizes the speech synthesis, circumventingunintelligible gibberish even for synthesizing speech of unseen speakers. Inaddition, the model can scale out to an arbitrary number of reference audios toimprove the quality of the synthesized speech. According to our experiments,including a human evaluation, the proposed model significantly outperformsstate-of-the-art models when generating speech for unseen speakers in terms ofspeaker similarity and quality.

Attentron: Few-Shot Text-to-Speech Utilizing Attention-Based Variable-Length Embedding