  This paper describes progress towards making a Neural Text-to-Speech (TTS)
Frontend that works for many languages and can be easily extended to new
languages. We take a Machine Translation (MT) inspired approach to constructing
the frontend, and model both text normalization and pronunciation on a sentence
level by building and using sequence-to-sequence (S2S) models. We experimented
with training normalization and pronunciation as separate S2S models and with
training a single S2S model combining both functions.
  For our language-independent approach to pronunciation we do not use a
lexicon. Instead all pronunciations, including context-based pronunciations,
are captured in the S2S model. We also present a language-independent chunking
and splicing technique that allows us to process arbitrary-length sentences.
Models for 18 languages were trained and evaluated. Many of the accuracy
measurements are above 99%. We also evaluated the models in the context of
end-to-end synthesis against our current production system.
