ABSTRACT

This paper presents the task of normalizing Vietnamese transcribed texts in Speech-to-Text (STT) systems. The main purpose is to develop a text normalizer that automatically converts proper nouns and other context-specific formatting of the transcription such as dates, time, and numbers into their appropriate expressions. To this end, we propose a solution that exploits deep neural networks with rich features followed by manually designed rules to recognize and then convert these text sequences. We also introduce a new corpus of 13 K spoken sentences to facilitate the process of the text normalization. The experimental results on this corpus are quite promising. The proposed method yields 90.67% in the F1 score in recognizing sequences of texts that need converting. We hope that this initial work will inspire other follow-up research on this important but unexplored problem.

摘要

本文介绍了在语音转文本（STT）系统中规范越南语转录文本的任务。主要目的是开发一种文本规范化器，该规范化器可自动将专有名词和转录的其他特定于上下文的格式（例如日期，时间和数字）转换为它们的合适表达形式。为此，我们提出了一种解决方案，该解决方案利用具有丰富功能的深层神经网络，然后通过人工设计的规则来识别并转换这些文本序列。我们还引入了一个新的13 K口语语料库，以促进文本规范化的过程。该语料库的实验结果很有希望。所提出的方法在识别需要转换的文本序列时在F1分数中产生90.67％。