Investigating the role of machine translated text in ASR domain adaptation: Unsupervised and semi-supervised methods

Abstract

This study investigates the use of machine translated text for ASR domain adaptation. The proposed methodology is applicable when domain-specific data is available in language X only, whereas the goal is to develop a domain-specific system in language Y. Two semi-supervised methods are introduced and compared with a fully unsupervised approach, which represents the baseline. While both unsupervised and semi-supervised approaches allow to quickly develop an accurate domain-specific ASR system, the semi-supervised approaches overpass the unsupervised one by 10% to 29% relative, depending on the amount of human post-processed data available. An in-depth analysis, to explain how the machine translated text improves the performance of the domain-specific ASR, is also given at the end of this paper.