# Conditioning and Sampling in Variational Diffusion Models for Speech Super-Resolution Source: [Conditioning and Sampling in Variational Diffusion Models for Speech Super-Resolution]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:31:06.502Z Authors: Chin-Yun Yu, Sung-Lin Yeh, Gy\\"orgy Fazekas, Hao Tang Published: 2022-10-27T22:31:20.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Audio Super-Resolution on VCTK In-domain](): Audio Super-Resolution; VCTK In-domain; LSD: 0.65. ## Original sources and implementation [Original paper]() [PDF]()