  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.
