  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
https://haoheliu.github.io/nvsr.
