SE-Bridge: Speech Enhancement with Consistent Brownian Bridge

• URL: http://arxiv.org/abs/2305.13796v1
• Date: Tue, 23 May 2023 08:06:36 GMT
• Title: SE-Bridge: Speech Enhancement with Consistent Brownian Bridge
• Authors: Zhibin Qiu, Mengfan Fu, Fuchun Sun, Gulila Altenbek, Hao Huang
• Abstract summary: We propose SE-Bridge, a novel method for speech enhancement (SE) Our approach is based on consistency model that ensure any speech states on the same PF-ODE trajectory, correspond to the same initial state. By integrating the Brownian Bridge process, the model is able to generate high-intelligibility speech samples without adversarial training.
• Score: 18.37042387650827
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose SE-Bridge, a novel method for speech enhancement (SE). After recently applying the diffusion models to speech enhancement, we can achieve speech enhancement by solving a stochastic differential equation (SDE). Each SDE corresponds to a probabilistic flow ordinary differential equation (PF-ODE), and the trajectory of the PF-ODE solution consists of the speech states at different moments. Our approach is based on consistency model that ensure any speech states on the same PF-ODE trajectory, correspond to the same initial state. By integrating the Brownian Bridge process, the model is able to generate high-intelligibility speech samples without adversarial training. This is the first attempt that applies the consistency models to SE task, achieving state-of-the-art results in several metrics while saving 15 x the time required for sampling compared to the diffusion-based baseline. Our experiments on multiple datasets demonstrate the effectiveness of SE-Bridge in SE. Furthermore, we show through extensive experiments on downstream tasks, including Automatic Speech Recognition (ASR) and Speaker Verification (SV), that SE-Bridge can effectively support multiple downstream tasks.

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