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

TL;DR

This paper presents an improved SEGAN model that effectively transforms low-quality device-recorded speech into high-quality speech, demonstrating significant enhancement in speech quality through a new dataset and robust training modifications.

Contribution

The paper introduces an enhanced SEGAN-based approach with stability improvements and a new dataset for transforming low-quality speech into high-quality audio.

Findings

Significant speech quality improvements shown in listening tests

Robust and stable training of the enhanced SEGAN model

Effective transformation from low to high-quality speech signals

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.