2024/07/22 by Jukić, Ante, Korostik, Roman, Balam, Jagadeesh +1 · 16 citations
#Audio and Speech Processing (eess.AS) #FOS: Electrical engineering #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2407.16074
This paper proposes a generative speech enhancement model based on Schrödinger 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.