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

• URL: http://arxiv.org/abs/2203.15479v1
• Date: Tue, 29 Mar 2022 12:26:56 GMT
• Title: Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation
• Authors: Ryo Fukuda, Katsuhito Sudoh, Satoshi Nakamura
• Abstract summary: We propose a speech segmentation method using a binary classification model trained using a segmented bilingual speech corpus. Experimental results revealed that the proposed method is more suitable for cascade and end-to-end ST systems than conventional segmentation methods.
• Score: 16.630616128169372
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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