# Schr"odinger Bridge for Generative Speech Enhancement

arXiv: https://arxiv.org/abs/2407.16074 · HTML: https://mlsanity.com/paper/2407.16074/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2024.

## Built on by (most cited first, 27 of 27)

- [A2SB: Audio-to-Audio Schrodinger Bridges](https://mlsanity.com/paper/2501.11311/) (2025)
- [Diffusion-based Speech Enhancement with Schr"odinger Bridge and Symmetric Noise Schedule](https://mlsanity.com/paper/2409.05116/) (2024)
- [MeanFlowSE: one-step generative speech enhancement via conditional mean flow](https://mlsanity.com/paper/2509.14858/) (2025)
- [A Composite Predictive-Generative Approach to Monaural Universal Speech Enhancement](https://mlsanity.com/paper/2505.24576/) (2025)
- [Robust One-step Speech Enhancement via Consistency Distillation](https://mlsanity.com/paper/2507.05688/) (2025)
- [Schr"odinger Bridge Consistency Trajectory Models for Speech Enhancement](https://mlsanity.com/paper/2507.11925/) (2025)
- [VoiceBridge: General Speech Restoration with One-step Latent Bridge Models](https://mlsanity.com/paper/2509.25275/) (2025)
- [Real-Time Streamable Generative Speech Restoration with Flow Matching](https://mlsanity.com/paper/2512.19442/) (2025)
- [Flowing Straighter with Conditional Flow Matching for Accurate Speech Enhancement](https://mlsanity.com/paper/2508.20584/) (2025)
- [Schr"odinger Bridge Mamba for One-Step Speech Enhancement](https://mlsanity.com/paper/2510.16834/) (2025)
- [LipDiffuser: Lip-to-Speech Generation with Conditional Diffusion Models](https://mlsanity.com/paper/2505.11391/) (2025)
- [ArtiFree: Detecting and Reducing Generative Artifacts in Diffusion-based Speech Enhancement](https://mlsanity.com/paper/2509.19495/) (2025)
- [Diffusion Buffer for Online Generative Speech Enhancement](https://mlsanity.com/paper/2510.18744/) (2025)
- [DeepFilterGAN: A Full-band Real-time Speech Enhancement System with GAN-based Stochastic Regeneration](https://mlsanity.com/paper/2505.23515/) (2025)
- [Audio Generation Through Score-Based Generative Modeling: Design Principles and Implementation](https://mlsanity.com/paper/2506.08457/) (2025)
- [Regularized Schr"odinger Bridge via Distortion-Perception Perturbation for High-Fidelity Speech Enhancement](https://mlsanity.com/paper/2511.11686/) (2025)
- [Investigating training objective for flow matching-based speech enhancement](https://mlsanity.com/paper/2512.10382/) (2025)
- [Extract and Diffuse: Latent Integration for Improved Diffusion-based Speech and Vocal Enhancement](https://mlsanity.com/paper/2409.09642/) (2024)
- [Combining Deterministic Enhanced Conditions with Dual-Streaming Encoding for Diffusion-Based Speech Enhancement](https://mlsanity.com/paper/2505.13983/) (2025)
- [On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation](https://mlsanity.com/paper/2508.18833/) (2025)
- [BridgeVoC: Revitalizing Neural Vocoder from a Restoration Perspective](https://mlsanity.com/paper/2511.07116/) (2025)
- [A Fast Solver for Interpolating Stochastic Differential Equation Diffusion Models for Speech Restoration](https://mlsanity.com/paper/2603.09508/) (2026)
- [Predictive-Generative Drift Decomposition for Speech Enhancement and Separation](https://mlsanity.com/paper/2605.06189/) (2026)
- [SB-RF: Schr"odinger Bridge Rectified Flow for One-Step Robust Speech Enhancement](https://mlsanity.com/paper/2606.05575/) (2026)
- [HybridSB-MoE: Dual-Domain Schr"odinger Bridges with Scene-Adaptive Expert Routing for Speech Enhancement](https://mlsanity.com/paper/2608.12715/) (2026)
- [Corrective Forcing: Unified Post-Training for Diffusions and Flows in Generative Speech Enhancement](https://mlsanity.com/paper/2609.24651/) (2026)
- [Perceptual Quality Loss or Loss of Perceptual Quality?](https://mlsanity.com/paper/2609.35054/) (2026)

## Abstract (arXiv)

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.