  On account of growing demands for personalization, the need for a so-called
few-shot TTS system that clones speakers with only a few data is emerging. To
address this issue, we propose Attentron, a few-shot TTS model that clones
voices of speakers unseen during training. It introduces two special encoders,
each serving different purposes. A fine-grained encoder extracts
variable-length style information via an attention mechanism, and a
coarse-grained encoder greatly stabilizes the speech synthesis, circumventing
unintelligible gibberish even for synthesizing speech of unseen speakers. In
addition, the model can scale out to an arbitrary number of reference audios to
improve the quality of the synthesized speech. According to our experiments,
including a human evaluation, the proposed model significantly outperforms
state-of-the-art models when generating speech for unseen speakers in terms of
speaker similarity and quality.
