arXiv:2603. 23723v2 Announce Type: replace-cross Abstract: Deep spatially selective filters achieve high-quality enhancement with real-time capable architectures for stationary speakers of known directions.
By Jakob Kienegger, Timo Gerkmann
The paper introduces SNAP, a speaker‑nulling framework designed to improve deepfake speech detection. By estimating a speaker subspace and orthogonally projecting out speaker‑dependent components, SNAP isolates synthesis artifacts in the residual features. This reduction of speaker entanglement enables detectors to focus on artifact‑related cues, achieving state‑of‑the‑art performance.
By Kyudan Jung, Jihwan Kim, Minwoo Lee, Soyoon Kim, Jeonghoon Kim, Jaegul Choo, Cheonbok Park
arXiv:2607. 00363v1 Announce Type: cross Abstract: Flow Matching (FM) has emerged as a powerful paradigm for speech generation but remains constrained by high inference latency and timbre leakage.
By Zuda Yu, Qianhui Xu, Ting Chen, Junhui Zhang, Tao Fu, Hongjiang Yu, Qiangqing Wang, Yang Song
arXiv:2606. 09677v1 Announce Type: cross Abstract: While discriminative models for multi-channel speech separation excel in reference-based metrics, they often exhibit suboptimal human listening quality.
By Dohwan Kim, Jung-Woo Choi
arXiv:2606. 27320v1 Announce Type: cross Abstract: Neural audio autoencoders have become a core component of compression, feature extraction, and generation.
By Dimitrios Bralios, Paris Smaragdis, Minje Kim
arXiv:2607. 13110v1 Announce Type: cross Abstract: Since the paradigm centered on convolutional neural networks and recurrent architectures was established in 2020, the fundamental backbone networks for audio-visual navigation have undergone no essential changes for more than five years, making them inadequate to support efficient representation of dynamic multimodal sequences.
By Yi Wang, Yinfeng Yu