  In this work, we explore multiple architectures and training procedures for
developing a multi-speaker and multi-lingual neural TTS system with the goals
of a) improving the quality when the available data in the target language is
limited and b) enabling cross-lingual synthesis. We report results from a large
experiment using 30 speakers in 8 different languages across 15 different
locales. The system is trained on the same amount of data per speaker. Compared
to a single-speaker model, when the suggested system is fine tuned to a
speaker, it produces significantly better quality in most of the cases while it
only uses less than $40\%$ of the speaker's data used to build the
single-speaker model. In cross-lingual synthesis, on average, the generated
quality is within $80\%$ of native single-speaker models, in terms of Mean
Opinion Score.
