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arXiv · 2407.16074

Schr\"odinger Bridge for Generative Speech Enhancement

Ante Jukić·Roman Korostik·Jagadeesh Balam·Boris Ginsburg
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Abstract

This paper proposes a generative speech enhancement model based on Schr\"odinger bridge (SB). The proposed model is employing a tractable SB to formulate a data-to-data process between the clean speech distribution and the observed noisy speech distribution. The model is trained with a data prediction loss, aiming to recover the complex-valued clean speech coefficients, and an auxiliary time-domain loss is used to improve training of the model. The effectiveness of the proposed SB-based model is evaluated in two different speech enhancement tasks: speech denoising and speech dereverberation. The experimental results demonstrate that the proposed SB-based outperforms diffusion-based models in terms of speech quality metrics and ASR performance, e.g., resulting in relative word error rate reduction of 20% for denoising and 6% for dereverberation compared to the best baseline model. The proposed model also demonstrates improved efficiency, achieving better quality than the baselines for the same number of sampling steps and with a reduced computational cost.

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BibTeXRIS

Ante Jukić, Roman Korostik, Jagadeesh Balam, Boris Ginsburg. 2024-07-22. Schr\"odinger Bridge for Generative Speech Enhancement. https://arxiv.org/abs/2407.16074

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