arXiv AI August 14, 2026 By Zhengyi Lu, Aswini Sivakumar, Jie Hu, Yao Qiang

HybridSB-MoE: Dual-Domain Schr\"odinger Bridges with Scene-Adaptive Expert Routing for Speech Enhancement

arXiv:2608. 12715v1 Announce Type: cross Abstract: Generative speech enhancement faces three gaps: spectral models capture harmonic structure but often disrupt phase, waveform models preserve phase but miss harmonics, and Schr\"odinger Bridges (SB) shorten transport from noise to clean speech but leave inference cost only loosely tied to training.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 4

Regularized Schr\"odinger Bridge via Distortion-Perception Perturbation for High-Fidelity Speech Enhancement

arXiv:2511. 11686v4 Announce Type: replace Abstract: Speech enhancement (SE) requires high-fidelity reconstruction of clean speech that preserves linguistic and paralinguistic cues while maintaining high perceptual quality.

By Qing Yao, Lijian Gao, Qirong Mao, Ming Dong

diffusion safety

arXiv AI

Jun 9

Speech Enhancement Based on Drifting Models

arXiv:2604. 24199v4 Announce Type: replace-cross Abstract: We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem.

By Liang Xu, Diego Caviedes-Nozal, W. Bastiaan Kleijn, Longfei Felix Yan, Rasmus Kongsgaard Olsson

diffusion benchmarks

arXiv AI

Jun 16

SPRI: SVD-Partitioned Residual Initialization for Data-Constrained MoE Upcycling

arXiv:2606. 16456v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models enable efficient scaling, but training them from scratch remains prohibitively expensive.

By Weiqiao Shan, Ruixiang Mao, Yuang Li, Yuhao Zhang, Yingfeng Luo, Tong Zheng, Chen Xu, Yucheng Qiao, Chunxiang Jin, Yi Yuan, Jingdong Chen, Tong Xiao, Jingbo Zhu

fine-tuning

arXiv Machine Learning

Jun 24

NeuroSonic: Conditional Flow Matching for EEG-to-Speech Reconstruction

arXiv:2606. 24087v1 Announce Type: new Abstract: Reconstructing continuous speech from scalp electroencephalography (EEG) remains fundamentally challenging.

By Wenhao Gao, Yifan Wang, Yijia Ma, Carl Yang, Wen Li, Chenyu You

llms diffusion benchmarks

arXiv AI

Aug 5

PASE: Leveraging the Phonological Prior of WavLM for Low-Hallucination Generative Speech Enhancement

arXiv:2511. 13300v1 Announce Type: cross Abstract: Generative models have shown remarkable performance in speech enhancement (SE), achieving superior perceptual quality over traditional discriminative approaches.

By Xiaobin Rong, Qinwen Hu, Mansur Yesilbursa, Kamil Wojcicki, Jing Lu

llms efficiency benchmarks safety

arXiv AI

Aug 11

CuteTTS: Efficient and High-Quality Speech Synthesis via Autoregressive Modeling of Continuous Latents

arXiv:2608. 08638v1 Announce Type: cross Abstract: Zero-shot text-to-speech (TTS) now supports interactive assistants, personalized media, and accessibility tools.

By Yuqian Zhang, Yao Shi, Kexin Huang, Botian Jiang, Zhe Xu, Yiwei Zhao, Min Liang, Shuang Chen, Xipeng Qiu

diffusion efficiency multimodal

Related stories

Regularized Schr\"odinger Bridge via Distortion-Perception Perturbation for High-Fidelity Speech Enhancement

Speech Enhancement Based on Drifting Models

SPRI: SVD-Partitioned Residual Initialization for Data-Constrained MoE Upcycling

NeuroSonic: Conditional Flow Matching for EEG-to-Speech Reconstruction

PASE: Leveraging the Phonological Prior of WavLM for Low-Hallucination Generative Speech Enhancement

CuteTTS: Efficient and High-Quality Speech Synthesis via Autoregressive Modeling of Continuous Latents