Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation

Abstract

Speech segmentation, which splits long speech into short segments, is essential for speech translation (ST).Popular VAD tools like WebRTC VAD 1 have generally relied on pause-based segmentation.Unfortunately, pauses in speech do not necessarily match sentence boundaries, and sentences can be connected by a very short pause that is difficult to detect by VAD.In this study, we propose a speech segmentation method using a binary classification model trained using a segmented bilingual speech corpus.We also propose a hybrid method that combines VAD and the above speech segmentation method.Experimental results reveal that the proposed method is more suitable for cascade and end-to-end ST systems than conventional segmentation methods.The hybrid approach further improves the translation performance.

Locations

• arXiv (Cornell University) - View - PDF
• Interspeech 2022 - View