Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems

摘要

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 tok-enization 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 pre-coded 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.