The study examines how the realism of synthetic room impulse response (RIR) datasets influences the training of DeepFilterNet3 for single‑channel speech enhancement. By comparing a DNS4 image‑source‑method RIR set with a higher‑fidelity hybrid wave‑based and geometrical acoustics RIR set, the authors find that the more realistic dataset consistently improves objective speech enhancement metrics and significantly reduces ASR word error rates on unseen measured RIRs. The results suggest that overall realism in synthetic acoustic training data enhances DeepFilterNet3’s generalization to new environments.
By Alessia Milo, Georg G\"otz, Steinar Gu{\dh}j\'onsson, Daniel Gert Nielsen, Jesper Pedersen, Finnur Pind
arXiv:2604. 14606v2 Announce Type: cross Abstract: Universal speech enhancement (USE) aims to restore speech signals from diverse distortions across multiple sampling rates.
By Xiaobin Rong, Zheng Wang, Yushi Wang, Jun Gao, Jing Lu
arXiv:2509. 15210v2 Announce Type: replace-cross Abstract: Realistic sound simulation plays a critical role in many applications.
By Chen Si, Qianyi Wu, Chaitanya Amballa, Romit Roy Choudhury
The paper proposes a single‑utterance test‑time adaptation (TTA) method for speech enhancement that uses an autoregressive prior trained on clean speech latent representations from a neural audio codec. The adaptation regularizes a pretrained enhancement model by minimizing the Kullback‑Leibler divergence between the enhanced speech distribution and the clean speech prior. Experiments on multiple noisy speech datasets demonstrate consistent improvements in speech quality, especially when training and testing noise conditions differ.
By Sofiene Kammoun, Simon Leglaive, Xavier Alameda-Pineda, Timo Gerkmann
arXiv:2608. 09288v1 Announce Type: cross Abstract: Audio-visual speech enhancement under real-world conditions remains challenging due to unreliable visual inputs and the lack of large-scale training data with realistic acoustic conditions.
By Wei Zhou, Wanyi Ning, Yinshang Guo, Qianxiao Fang, Haitao Qian, Yingpeng Li
arXiv:2609.37116v1 Announce Type: cross
Abstract: Accurate perceptual quality assessment is essential for evaluating and optimizing spatial audio, where perceived quality depends on both signal fidel...
By Gouthaman KV, Shiv Gehlot, Vishnu Raj, Lars Villemoes, Arijit Biswas
The paper introduces a simulation-based method for detecting a user's own voice in hearing aids using only a single microphone. It employs a data augmentation strategy with simulated acoustic transfer functions to train a transformer classifier, achieving over 90% accuracy on both simulated and real-world recordings. The approach reduces hardware complexity and power consumption while maintaining robust performance across varied spatial conditions.
By Mathuranathan Mayuravaani, W. Bastiaan Kleijn, Andrew Lensen, Charlotte S{\o}rensen
arXiv:2606. 29031v1 Announce Type: cross Abstract: In regulated domains such as banking and healthcare, where privacy constraints make real speech costly to collect and retain, synthetic speech from modern text-to-speech (TTS) is an appealing alternative for training automatic speech recognition (ASR) without exposing sensitive customer recordings.
By Yanis Labrak, Dairazalia Sanchez-Cortes, Sergio Burdisso, S\'everin Baroudi, Shashi Kumar, Esa\'u Villatoro-Tello, Srikanth Madikeri, Manjunath K E, Old\v{r}ich Plchot, Kadri Hacio\u{g}lu, Petr Motlicek, Andreas Stolcke
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:2606. 27701v1 Announce Type: cross Abstract: While voice control is rapidly becoming a ubiquitous vector of human-AI communication, the risks facing these systems remain poorly understood.
By Andrew C. Cullen, Neil Marchant, Jiani Xie, Paul Montague, Benjamin I. P. Rubinstein
arXiv:2604. 01832v1 Announce Type: cross Abstract: We introduce GAP-URGENet, a generative-predictive fusion framework developed for Track 1 of the ICASSP 2026 URGENT Challenge.
By Xiaobin Rong, Yushi Wang, Zheng Wang, Jing Lu
NOPE-HYPE is a structured training workflow that integrates a controllable environment simulator, a coverage‑optimal reduction of Power Spectral Density templates, and a concise hyperparameter search over simulator settings. The authors demonstrate that noise generated by the simulator can match the performance of balanced real‑noise training for Whisper and SeamlessM4T models. They also provide principled environment prototype sets and practical default simulator configurations derived from a 27‑run hyperparameter sweep.
By Niramay M. Patel, Bibek Behera, Raksha Sharma