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.

使用少于 3% 英文数据的量，我们提出了一种新颖的体系结构来促进多种语言的 TTS 文本归一化系统，将 TN 视为一个序列分类问题，并提出了一种细粒度的分词机制，该机制使系统能够从训练数据本身学习大部分类及其归一化，同时结合最少的预先编码的语言知识来处理其他类别，我们发表了 TN 在西班牙语和泰米尔语 TTS 中的首个结果，并证明了该方法的性能与英文上的以前工作相当。

Proteno：基于少量数据的文本归一化技术，用于快速部署文本转语音系统