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Gesper: A Unified Framework for General Speech Restoration

Jun Chen (Tsinghua University); yupeng shi (tencent); wenzhe liu (Tencent); Wei Rao (Tencent); shulin 何 (Tencent); Andong Li (Institute of Acoustics, Chinese Academy of Sciences); Yannan Wang (Tencent); Zhiyong Wu (Tsinghua University); Shi-dong Shang (tencent); Chengshi Zheng (Chinese Academy of Science)

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10 Jun 2023

This paper describes the legends-tencent team's real-time General Speech Restoration (Gesper) system submitted to the ICASSP 2023 Speech Signal Improvement (SSI) Challenge.This newly proposed system is a two-stage architecture, in which the speech restoration is performed, and then followed by speech enhancement. We propose a complex spectral mapping-based generative adversarial network (CSM-GAN) as the speech restoration module for the first time. For noise suppression and dereverberation, the enhancement module is presented with fullband-wideband parallel processing. On the blind test set of ICASSP 2023 SSI Challenge, the proposed Gesper system, which satisfies the real-time condition, achieves 3.27 P.804 overall mean opinion score (MOS) and 3.35 P.835 overall MOS, ranked 1st in both track 1 and track 2.

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