This paper presents a challenge to the community: given a large corpus of written text aligned to its normalized spoken form, train an RNN to learn the correct normalization function. We present a data set of general text where the normalizations were generated using an existing text normalization component of a text-to-speech system. This data set will be released open-source in the near future. We also present our own experiments with this data set with a variety of different RNN architectures. While some of the architectures do in fact produce very good results when measured in terms of overall accuracy, the errors that are produced are problematic, since they would convey completely the wrong message if such a system were deployed in a speech application. On the other hand, we show that a simple FST-based filter can mitigate those errors, and achieve a level of accuracy not achievable by the RNN alone. Though our conclusions are largely negative on this point, we are actually not arguing that the text normalization problem is intractable using an pure RNN approach, merely that it is not going to be something that can be solved merely by having huge amounts of annotated text data and feeding that to a general RNN model. And when we open-source our data, we will be providing a novel data set for sequence-to-sequence modeling in the hopes that the the community can find better solutions.

该论文介绍了一个挑战：给定一个大型的文本语音对齐数据集，通过训练递归神经网络(RNN)来学习正确的文本归一化函数。论文提供了一个通用文本数据集，其中归一化是使用现有的文本到语音系统的文本归一化组件生成的，并表明简单的FST过滤器可以减轻RNN产生的错误，旨在为序列到序列建模提供一个新的数据集，以期寻找更好的解决方案。

文本标准化的循环神经网络方法：一项挑战