Spoken‐to‐written text conversion for enhancement of Korean–English readability and machine translation

Abstract

The Korean language has written (formal) and spoken (phonetic) forms that differ in their application, which can lead to confusion, especially when dealing with numbers and embedded Western words and phrases. This fact makes it difficult to automate Korean speech recognition models due to the need for a complete transcription training dataset. Because such datasets are frequently constructed using broadcast audio and their accompanying transcriptions, they do not follow a discrete rule‐based matching pattern. Furthermore, these mismatches are exacerbated over time due to changing tacit policies. To mitigate this problem, we introduce a data‐driven Korean spoken‐to‐written transcription conversion technique that enhances the automatic conversion of numbers and Western phrases to improve automatic translation model performance.

Special issue on speech and language AI technologies

“…The use of high-quality and adequate data for addressed application tasks is key to achieve stable high performance. The tenth paper in this special issue [10], "Spoken-to-written text conversion for enhancement of Korean-English readability and machine translation" by Choi and others, addresses the problem that Korean text produced by automatic speech recognition is often not presented in the written but in the spoken form, particularly when including numeric expressions and English words. Consequently, frequent ambiguities occur in similar types of errors for automatic speech translation.…”