  Recently, diffusion models (DMs) have been increasingly used in audio
processing tasks, including speech super-resolution (SR), which aims to restore
high-frequency content given low-resolution speech utterances. This is commonly
achieved by conditioning the network of noise predictor with low-resolution
audio. In this paper, we propose a novel sampling algorithm that communicates
the information of the low-resolution audio via the reverse sampling process of
DMs. The proposed method can be a drop-in replacement for the vanilla sampling
process and can significantly improve the performance of the existing works.
Moreover, by coupling the proposed sampling method with an unconditional DM,
i.e., a DM with no auxiliary inputs to its noise predictor, we can generalize
it to a wide range of SR setups. We also attain state-of-the-art results on the
VCTK Multi-Speaker benchmark with this novel formulation.
