Speech segmentation, which splits long speech into short segments, is essential for speech translation (ST). Popular VAD tools like WebRTC VAD 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 revealed that the proposed method is more suitable for cascade and end-to-end ST systems than conventional segmentation methods. The hybrid approach further improved the translation performance.
@article{arxiv.2203.15479,
title = {Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation},
author = {Ryo Fukuda and Katsuhito Sudoh and Satoshi Nakamura},
journal= {arXiv preprint arXiv:2203.15479},
year = {2022}
}