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arXiv · 2508.06840

FlowSE: Flow Matching-based Speech Enhancement

Seonggyu Lee·Sein Cheong·Sangwook Han·Jong Won Shin
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Abstract

Diffusion probabilistic models have shown impressive performance for speech enhancement, but they typically require 25 to 60 function evaluations in the inference phase, resulting in heavy computational complexity. Recently, a fine-tuning method was proposed to correct the reverse process, which significantly lowered the number of function evaluations (NFE). Flow matching is a method to train continuous normalizing flows which model probability paths from known distributions to unknown distributions including those described by diffusion processes. In this paper, we propose a speech enhancement based on conditional flow matching. The proposed method achieved the performance comparable to those for the diffusion-based speech enhancement with the NFE of 60 when the NFE was 5, and showed similar performance with the diffusion model correcting the reverse process at the same NFE from 1 to 5 without additional fine tuning procedure. We also have shown that the corresponding diffusion model derived from the conditional probability path with a modified optimal transport conditional vector field demonstrated similar performances with the NFE of 5 without any fine-tuning procedure.

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Seonggyu Lee, Sein Cheong, Sangwook Han, Jong Won Shin. 2025-08-09. FlowSE: Flow Matching-based Speech Enhancement. https://arxiv.org/abs/2508.06840

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