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

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

URL: http://arxiv.org/abs/2104.07777v1
Date: Thu, 15 Apr 2021 21:14:28 GMT
Title: Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems
Authors: Shubhi Tyagi, Antonio Bonafonte, Jaime Lorenzo-Trueba, Javier Latorre
Abstract summary: 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 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.
Score: 15.401574286479546
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.