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:2510. 20441v2 Announce Type: replace-cross Abstract: Neural audio codecs have largely promoted the application of language models (LMs) for speech applications.
By Haoyin Yan, Chengwei Liu, Shaofei Xue, Xiaotao Liang, Yinghao Liu, Yuxiang Kong, Zheng Xue
StrixAE is an intelligent audio enhancement agent that uses a multimodal large language model to coordinate multiple enhancement and personalization models. It is trained in two stages: first, chain‑of‑thought supervised fine‑tuning on AcoustBench, and second, Audio Perception Reinforcement Learning that optimizes format validity, structural coherence, and perceptual quality. The approach yields state‑of‑the‑art performance and strong generalization on real‑world test datasets, outperforming many existing open‑source and proprietary solutions.
By Chenglin Wu, Junjie Wu, Jinhang Chen, Mingyang Chen, Zixu Lin, Jiabian Chen, Xinghao Ding, Xiaotong Tu
arXiv:2608. 09930v1 Announce Type: cross Abstract: Automated Text-to-Speech (TTS) evaluation methods (Mean Opinion Score (MOS) predictors and Audio Large Language Models (Audio-LLM) judges) are expected to reflect human perception, yet it is unclear how well they capture the distinct aspects of speech that listeners actually perceive.
By Oluwanifemi Bamgbose, Simon Rosen, Jash Shah, Lindsay Devon Brin, Hoang H Nguyen, Anke Koelzer, Rachel Hansen, Tara Bogavelli, Fanny Riols
arXiv:2608. 18607v2 Announce Type: replace Abstract: Using reinforcement learning to post-train joint video-audio generation models requires a reward signal.
By Yinming Huang, Shuyuan Tu, Xi Yan, Zihan Yang, Jianhua Han, Xu Hang, Yu-Gang Jiang, Zuxuan Wu
arXiv:2509. 14659v3 Announce Type: replace-cross Abstract: Current audio captioning relies on supervised learning with paired audio-caption data, which is costly to curate and may not reflect human preferences in real-world scenarios.
By Kartik Hegde, Rehana Mahfuz, Yinyi Guo, Erik Visser