  Developing Text Normalization (TN) systems for Text-to-Speech (TTS) on new
languages is hard. We propose a novel architecture to facilitate it for
multiple languages while using data less than 3% of the size of the data used
by the state of the art results on English. We treat TN as a sequence
classification problem and propose a granular tokenization mechanism that
enables the system to learn majority of the classes and their normalizations
from the training data itself. This is further combined with minimal precoded
linguistic knowledge for other classes. We publish the first results on TN for
TTS in Spanish and Tamil and also demonstrate that the performance of the
approach is comparable with the previous work done on English. All annotated
datasets used for experimentation will be released at
https://github.com/amazon-research/proteno.
