mdctGAN: Taming transformer-based GAN for speech super-resolution with Modified DCT spectra

TL;DR

mdctGAN is a novel speech super-resolution framework that leverages adversarial learning in the MDCT domain to produce high-quality, phase-aware speech reconstruction without vocoders, outperforming existing methods.

Contribution

The paper introduces mdctGAN, a phase-aware GAN-based SSR method using MDCT and self-attention, achieving state-of-the-art results without post-processing.

Findings

High MOS and PESQ scores on VCTK dataset

State-of-the-art LSD performance at 48 kHz

Effective phase-aware speech reconstruction

Abstract

Speech super-resolution (SSR) aims to recover a high resolution (HR) speech from its corresponding low resolution (LR) counterpart. Recent SSR methods focus more on the reconstruction of the magnitude spectrogram, ignoring the importance of phase reconstruction, thereby limiting the recovery quality. To address this issue, we propose mdctGAN, a novel SSR framework based on modified discrete cosine transform (MDCT). By adversarial learning in the MDCT domain, our method reconstructs HR speeches in a phase-aware manner without vocoders or additional post-processing. Furthermore, by learning frequency consistent features with self-attentive mechanism, mdctGAN guarantees a high quality speech reconstruction. For VCTK corpus dataset, the experiment results show that our model produces natural auditory quality with high MOS and PESQ scores. It also achieves the state-of-the-art log-spectral-distance (LSD) performance on 48 kHz target resolution from various input rates. Code is available from https://github.com/neoncloud/mdctGAN