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
this https URL

提出基于 MDCT 的 mdctGAN SSR 框架，通过敌对学习在 MDCT 域中以相位感知的方式重构高质量的语音，无需使用语音编解码器或其他额外的后处理，实验结果显示该模型在各种输入速率下，实现了 48 kHz 目标分辨率下的最新的对数组距离（LSD）性能。

使用修改的 DCT 频谱驯服基于 Transformer 的 GAN 进行语音超分辨率

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

Speech super-resolution (SR) is a task to increase speech sampling rate by
generating high-frequency components. Existing speech SR methods are trained in
constrained experimental settings, such as a fixed upsampling ratio. These
strong constraints can potentially lead to poor generalization ability in
mismatched real-world cases. In this paper, we propose a neural vocoder based
speech super-resolution method (NVSR) that can handle a variety of input
resolution and upsampling ratios. NVSR consists of a mel-bandwidth extension
module, a neural vocoder module, and a post-processing module. Our proposed
system achieves state-of-the-art results on the VCTK multi-speaker benchmark.
On 44.1 kHz target resolution, NVSR outperforms WSRGlow and Nu-wave by 8% and
37% respectively on log spectral distance and achieves a significantly better
perceptual quality. We also demonstrate that prior knowledge in the pre-trained
vocoder is crucial for speech SR by performing mel-bandwidth extension with a
simple replication-padding method. Samples can be found in
this https URL.

本文提出了一种基于神经声码器的语音超分辨率方法 (NVSR)，该方法可以处理各种输入分辨率和上采样比例，其取得了优于当前最先进方法 WSRGlow 和 Nu-wave 的 8% 和 37% 的对数谱距离精度，并且具有显著更好的感知质量。