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Neural Text Normalization in Speech-to-Text Systems with Rich Features

April 16, 2024

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Authors: Oanh Thi Tran, Viet The Bui

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 another 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 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.

Published: 11 Jan 2021

DOI: 10.1080 / 08839514.2020.1842108

This research is funded by International School, Vietnam National University, Hanoi (VNU-IS) under project number CS.NNC/2020-05.

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