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:2607. 03134v1 Announce Type: cross Abstract: Recent research expands beyond binary anti-spoofing with the emergence of Source Tracing, the task of identifying the specific generative origins of synthetic speech.
By Santiago Rubio, Antonio Almud\'evar, Antonio Miguel, Eduardo Lleida, Alfonso Ortega
arXiv:2604. 19635v2 Announce Type: replace-cross Abstract: While generative models have set new benchmarks for Target Speaker Extraction (TSE), their inherent reliance on global context precludes deployment in real-time applications.
By Shuhai Peng, Hui Lu, Jinjiang Liu, Liyang Chen, Guiping Zhong, Jiakui Li, Huimeng Wang, Haiyun Li, Liang Cao, Shiyin Kang, Zhiyong Wu
arXiv:2601. 09239v5 Announce Type: replace-cross Abstract: Speech tokenizers are a key building block of fully discrete Speech LLMs.
By Hanlin Zhang, Daxin Tan, Dehua Tao, Xiao Chen, Haochen Tan, Yunhe Li, Yuchen Cao, Linqi Song
arXiv:2607. 09833v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) systems have achieved high accuracy with transformer-based models, enabling deployment in critical applications.
By Yanis Xabier Wilbrand Pe\~na, Oliver Wei{\ss}l, Andrea Stocco
The paper introduces Grounded Generative Speech Recognition (GSR), redefining speech recognition as generating semantically grounded text rather than a single transcript. It defines a grounded reformulation space and a four-level hierarchy of reformulation capabilities, and presents GSR-Bench, a comprehensive benchmark covering diverse scenarios such as meeting transcription, cross-dialect translation, and robustness to silence or noise. The authors also develop a multi-dimensional evaluation framework that combines speech grounding assessment with LLM-based semantic judgment, validated by human preference studies, and analyze trade-offs and failure modes across various ASR architectures.
By Qing Wang, Qihan Hu, Hang Lv, Yan Yang, Boqing Liu, Xuechao Zhu, Zehan Li, Hongjie Chen, Jian Kang, Jie Li, Yongxiang Li, Lei Xie
arXiv:2606. 14788v1 Announce Type: cross Abstract: Voice-based screening offers a scalable and non-invasive way to assess neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), but their staging remains challenging due to the difficulty of integrating heterogeneous data.
By Qingfeng Zhang, Yuanxiong Guo, Yanmin Gong
arXiv:2603.26246v2 Announce Type: replace-cross
Abstract: Standard LLM-based speech recognition systems process utterances in isolation, limiting their ability to leverage conversational context. Her...
By Shashi Kumar, Esa\'u Villatoro-Tello, Sergio Burdisso, Kadri Hacioglu, Thibault Ba\~neras-Roux, Hasindri Watawana, Dairazalia Sanchez-Cortes, Srikanth Madikeri, Petr Motlicek, Andreas Stolcke
PALATE is a new framework that improves the evaluation of deep generative models by applying the law of total expectation to accessible data. It builds on the Feature Likelihood Divergence (FLD) method but overcomes FLD’s computational limits, integrating with the MMD baseline metric and the DINOv3 feature extractor. Experiments show PALATE matches or surpasses state‑of‑the‑art methods while being more efficient and scalable, especially for detecting memorization and assessing generalization.
By Tadeusz Dziarmaga, Marcin K\k{a}dzio{\l}ka, Laura Musio{\l}, Patryk Marsza{\l}ek, Artur Kasymov, Piotr Ko\'scielniak, Marcin Mazur
arXiv:2602.09581v2 Announce Type: replace-cross
Abstract: Deep generative models that can tractably compute input likelihoods, including normalizing flows, often assign unexpectedly high likelihoods...
By Donghwan Kim, Hyunsoo Yoon
Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, multi-stream transcriptions, or learnable speaker embeddings. These systems operate within speech-only pipelines that produce clean vocal sequences without the ambient texture of real conversations.
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