The paper introduces Corrective Forcing (CoF), a post‑training method that aligns diffusion and flow generative models for speech enhancement by training them on self‑generated rollout states. CoF corrects predictions toward ground truth under dynamic sampling schedules and regularizes local evolution with counterfactual transitions, applying a unified objective across both model types. Experiments on SB‑VE and OT‑CFM show improved perceptual quality, reconstruction fidelity, and robustness to varying sampling steps.
By Qing Yao, Lijian Gao, Qirong Mao
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
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.
By Zhengyi Lu, Aswini Sivakumar, Jie Hu, Yao Qiang
arXiv:2512. 20978v2 Announce Type: replace-cross Abstract: Language Model (LM)-based generative modeling has emerged as a promising direction for TSE, offering potential for improved generalization and high-fidelity speech.
By Haoyang Li, Xuyi Zhuang, Azmat Adnan, Ye Ni, Wei Rao, Shreyas Gopal, Eng Siong Chng, Boon Siew Han, Yuanjin Zheng
arXiv:2609.13909v1 Announce Type: cross
Abstract: Continuous-latent Autoregressive Diffusion Transformer (AR-DiT) models have demonstrated immense potential in zero-shot speech generation. However, t...
By Ziyu Zhang, Tianlun Zuo, Hanzhao Li, Haoyu Zhang, Lei Xie
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
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
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
arXiv:2504.11809v2 Announce Type: replace
Abstract: Simultaneous speech translation (SimulST) produces translations incrementally while processing partial speech input. Although large language models...
By Biao Fu, Donglei Yu, Minpeng Liao, Chengxi Li, Xinjie Chen, Yidong Chen, Kai Fan, Xiaodong Shi
arXiv:2606. 09048v1 Announce Type: cross Abstract: Removing intermediate representations and separately trained decoding stages has become an important direction in generative modeling.
By Wei Fan, Chao-Hong Tan, Qian Chen, Wen Wang, Xiangang Li, Kejiang Chen, Weiming Zhang, Nenghai Yu
The paper adapts Reinforce Adjoint Matching (RAM) to generative speech enhancement, allowing a pretrained model to be post‑trained on real recordings using weak supervision such as text transcripts. RAM shifts the model’s conditional distribution toward higher‑reward outputs by generating enhanced speech on‑policy, evaluating each output with a potentially non‑differentiable reward, and analytically re‑noising the endpoint to create inputs for a reward‑guided regression objective. Experiments on real CHiME‑4 recordings show a 5.08‑percentage‑point reduction in word error rate compared to the pretrained FlowSE model, while maintaining all reported non‑intrusive speech quality metrics and receiving no significant preference in a subjective listening test.
By Julius Richter, Christoph Boeddeker, Yoshiki Masuyama, Kohei Saijo, Dominik Klement, Gordon Wichern, Jonathan Le Roux
arXiv:2609.37974v1 Announce Type: cross
Abstract: Masked diffusion models (MDMs) generate text by unmasking several tokens per step, but they are trained and sampled under different conditions. The m...
By Manuel Madeira, Amitis Shidani, Alice Bizeul, Victor Turrisi, Louis B\'ethune, Bhavika Devnani, Dan Busbridge, Pierre Ablin, Jo\~ao Monteiro