Transformation of low-quality device-recorded speech to high-quality speech using improved SEGAN model
Abstract
Nowadays vast amounts of speech data are recorded from low-quality recorder devices such as smartphones, tablets, laptops, and medium-quality microphones. The objective of this research was to study the automatic generation of high-quality speech from such low-quality device-recorded speech, which could then be applied to many speech-generation tasks. In this paper, we first introduce our new device-recorded speech dataset then propose an improved end-to-end method for automatically transforming the low-quality device-recorded speech into professional high-quality speech. Our method is an extension of a generative adversarial network (GAN)-based speech enhancement model called speech enhancement GAN (SEGAN), and we present two modifications to make model training more robust and stable. Finally, from a large-scale listening test, we show that our method can significantly enhance the quality of device-recorded speech signals.
Keywords
Cite
@article{arxiv.1911.03952,
title = {Transformation of low-quality device-recorded speech to high-quality speech using improved SEGAN model},
author = {Seyyed Saeed Sarfjoo and Xin Wang and Gustav Eje Henter and Jaime Lorenzo-Trueba and Shinji Takaki and Junichi Yamagishi},
journal= {arXiv preprint arXiv:1911.03952},
year = {2019}
}
Comments
This study was conducted during an internship of the first author at NII, Japan in 2017