  Recent neural Text-to-Speech (TTS) models have been shown to perform very
well when enough data is available. However, fine-tuning them for new speakers
or languages is not straightforward in a low-resource setup. In this paper, we
show that by applying minor modifications to a Tacotron model, one can transfer
an existing TTS model for new speakers from the same or a different language
using only 20 minutes of data. For this purpose, we first introduce a base
multi-lingual Tacotron with language-agnostic input, then demonstrate how
transfer learning is done for different scenarios of speaker adaptation without
exploiting any pre-trained speaker encoder or code-switching technique. We
evaluate the transferred model in both subjective and objective ways.
