  End-to-end text-to-speech (TTS) has shown great success on large quantities
of paired text plus speech data. However, laborious data collection remains
difficult for at least 95% of the languages over the world, which hinders the
development of TTS in different languages. In this paper, we aim to build TTS
systems for such low-resource (target) languages where only very limited paired
data are available. We show such TTS can be effectively constructed by
transferring knowledge from a high-resource (source) language. Since the model
trained on source language cannot be directly applied to target language due to
input space mismatch, we propose a method to learn a mapping between source and
target linguistic symbols. Benefiting from this learned mapping, pronunciation
information can be preserved throughout the transferring procedure. Preliminary
experiments show that we only need around 15 minutes of paired data to obtain a
relatively good TTS system. Furthermore, analytic studies demonstrated that the
automatically discovered mapping correlate well with the phonetic expertise.
