Diffusion-Based Speech Enhancement with Joint Generative and Predictive Decoders

Hao Shi*

Kazuki Shimada

Masato Hirano*

Takashi Shibuya

Yuichiro Koyama*

Zhi Zhong*

Shusuke Takahashi*

Tatsuya Kawahara*

Yuki Mitsufuji

* External authors

ICASSP 2024

2023

Abstract

Diffusion-based speech enhancement (SE) has been investigated recently, but its decoding is very time-consuming. One solution is to initialize the decoding process with the enhanced feature estimated by a predictive SE system. However, this two-stage method ignores the complementarity between predictive and diffusion SE. In this paper, we propose a unified system that integrates these two SE modules. The system encodes both generative and predictive information, and then applies both generative and predictive decoders, whose outputs are fused. Specifically, the two SE modules are fused in the first and final diffusion steps: the first step fusion initializes the diffusion process with the predictive SE for improving the convergence, and the final step fusion combines the two complementary SE outputs to improve the SE performance. Experiments on the Voice-Bank dataset show that the diffusion score estimation can benefit from the predictive information and speed up the decoding.