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arXiv · 1911.03952

Transformation of low-quality device-recorded speech to high-quality speech using improved SEGAN model

Seyyed Saeed Sarfjoo·Xin Wang·Gustav Eje Henter·Jaime Lorenzo-Trueba·Shinji Takaki·Junichi Yamagishi
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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.

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Seyyed Saeed Sarfjoo, Xin Wang, Gustav Eje Henter, Jaime Lorenzo-Trueba, Shinji Takaki, Junichi Yamagishi. 2019-11-20. Transformation of low-quality device-recorded speech to high-quality speech using improved SEGAN model. https://doi.org/10.7488/ds%2F1994

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