VorTEX: Various overlap ratio for Target speech EXtraction
- URL: http://arxiv.org/abs/2603.14803v3
- Date: Mon, 23 Mar 2026 03:26:41 GMT
- Title: VorTEX: Various overlap ratio for Target speech EXtraction
- Abstract summary: VorTEX is a text-prompted TSE architecture that separates primary extraction from auxiliary regularization pathways.<n>To enable controlled analysis, we construct PORTE, a two-speaker dataset spanning overlap ratios from 0% to 100%.<n> Experiments show that existing models exhibit suppression or residual interference under overlap, whereas VorTEX achieves the highest separation fidelity across 20-100% overlap.
- Score: 2.9248680865344343
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Target speech extraction (TSE) aims to recover a target speaker's voice from a mixture. While recent text-prompted approaches have shown promise, most approaches assume fully overlapped mixtures, limiting insight into behavior across realistic overlap ratios. We introduce VorTEX (Various overlap ratio for Target speech EXtraction), a text-prompted TSE architecture with a Decoupled Adaptive Multi-branch (DAM) Fusion block that separates primary extraction from auxiliary regularization pathways. To enable controlled analysis, we construct PORTE, a two-speaker dataset spanning overlap ratios from 0% to 100%. We further propose Suppression Ratio on Energy (SuRE), a diagnostic metric that detects suppression behavior not captured by conventional measures. Experiments show that existing models exhibit suppression or residual interference under overlap, whereas VorTEX achieves the highest separation fidelity across 20-100% overlap (e.g., 5.50 dB at 20% and 2.04 dB at 100%) while maintaining zero SuRE, indicating robust extraction without suppression-driven artifacts.
Related papers
- Mitigating Speaker Leakage in Cascaded Multi-talker ASR with Diarization-based Transcript Correction [0.8749675983608171]
cascaded multi-talker ASR (MT-ASR) performance is often capped by speaker leakage during separation.<n>We propose a complementary pruning-based paradigm that robustly identifies and removes leakage artifacts.<n>Results on LibriMix, LibriSpeechMix, and the AMI Meeting corpus show our algorithm consistently reduces cpW ER across diverse overlap conditions.
arXiv Detail & Related papers (2026-08-23T03:21:45Z) - Iterate or Widen? When Test-Time Refinement Helps LiDAR Scene Completion: A Controlled Study of Evidence Geometry, Training Coverage, and Compute [4.730664917323328]
We compare a one-shot predictor, a parameter-matched wider predictor, and a weight-tied multigridr from the same frozen predictor.<n>The protocol separates coherent region removal, independent thinning, range-dependent attenuation, and additive clutter.<n>Across five training seeds and 815 SemanticKITTI-08 frames, the full iterative system improves mIoU over the wide control by 0.911 points.
arXiv Detail & Related papers (2026-08-06T13:19:14Z) - Coupled Continuous-Discrete Generation for Scene Text Image Super-Resolution [51.31474187379532]
Scene text image super-resolution (STISR) aims to recover visually plausible appearance while preserving character semantics from degraded inputs.<n>We present DualTSR, a unified framework that formulates STISR as coupled continuous-discrete generation.<n> Conditional flow matching restores continuous image latents, while absorbing-state discrete diffusion reconstructs text tokens.
arXiv Detail & Related papers (2026-08-05T06:57:30Z) - Strength-Parity Ensembling with Parameter-Isolated Experts for Multi-Task Affect Recognition [4.4644619064521995]
We build on a shared affect-latent that marginalizes the missing labels across two affect-supervised backbones.<n>An added member lowers the ensemble error only when it is both decorrelated from the current members and a near-peer of them in individual accuracy.<n>The resulting system raises the overall validation score to 1.6949, against the organizersNeXt-with-Mixment baseline of 0.45.
arXiv Detail & Related papers (2026-07-12T17:12:15Z) - C$^{2}$R: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders [56.67585330425431]
Sparse Autoencoders (SAEs) are widely used to interpret large language models by decomposing activations into sparse, human-understandable features.<n>These issues stem from inconsistent latent assignment across samples.<n>We introduce C$2$R, which penalizes the co-activation of directionally similar latents.
arXiv Detail & Related papers (2026-06-29T17:45:31Z) - Dual-Granularity Orthogonal Disentanglement for Generalizable Audio Deepfake Detection [1.6262949941980935]
deepfake detectors often fail to generalize across speakers, as they learn speaker-identity features rather than synthesis artifacts.<n>This paper proposes a dual-granularity orthogonal disentanglement framework enforcing feature independence at two levels.<n> Experiments on ASVspoof 2019 LA, ASVspoof 2021 DF, and In-the-Wild datasets demonstrate that the proposed method achieves 1.35%, 7.88%, and 21.58% equal error rates.
arXiv Detail & Related papers (2026-06-15T10:36:57Z) - ANCHOR: Autoregressive Non-intrusive Chunk-Ordered Refinement for Joint Multi-Resolution Speech Quality Modeling [62.983372215479925]
Streaming and generative systems require incremental estimation from partial audio.<n>We propose ANCHOR, reformulating incremental assessment as a multi-resolution autore task.<n> Experiments show substantial robustness under partial input, including a 48% PLCMOS error reduction on 2-second prefixes.
arXiv Detail & Related papers (2026-06-08T22:46:30Z) - Soft Tuy-Completeness for Robust Projection Selection in Cone-Beam CT [4.213649496201482]
This work introduces a continuous soft near-orthogonality score and a resolution-aware saturated coverage objective for projection selection in region-of-interest focused cone-beam CT.
arXiv Detail & Related papers (2026-05-20T12:37:15Z) - Prune Redundancy, Preserve Essence: Vision Token Compression in VLMs via Synergistic Importance-Diversity [54.95089105944234]
Vision-language models (VLMs) face significant computational inefficiencies caused by excessive generation of visual tokens.<n>We propose PruneSID, a training-free Synergistic Importance-Diversity approach featuring a two-stage pipeline.<n>We demonstrate state-of-the-art performance, achieving 96.3% accuracy on LLaVA-1.5 with only 11.1% token retention, and 92.8% accuracy at extreme compression rates (5.6%) on LLaVA-NeXT.
arXiv Detail & Related papers (2026-03-10T10:31:58Z) - BadCLIP++: Stealthy and Persistent Backdoors in Multimodal Contrastive Learning [73.46118996284888]
Research on backdoor attacks against multimodal contrastive learning models faces two key challenges: stealthiness and persistence.<n>We propose BadCLIP++, a unified framework that tackles both challenges.<n>For stealthiness, we introduce a semantic-fusion QR micro-trigger that embeds imperceptible patterns near task-relevant regions.<n>For persistence, we stabilize trigger embeddings via radius shrinkage and centroid alignment.
arXiv Detail & Related papers (2026-02-19T08:31:16Z) - DiMEx: Breaking the Cold Start Barrier in Data-Free Model Extraction via Latent Diffusion Priors [0.0]
Model stealing attacks pose an existential threat to Machine Learning as a Service (ML)<n>We propose DiMEx, a framework that weaponizes the rich semantic priors of pre-trained Latent Diffusion Models to bypass the "Cold Start" problem.<n>Our results demonstrate that while DiMEx evades static distribution detectors, HSE exploits this temporal signature to suppress attack success rates to 21.6 percent with negligible latency.
arXiv Detail & Related papers (2026-01-04T22:58:34Z) - GateFusion: Hierarchical Gated Cross-Modal Fusion for Active Speaker Detection [19.80016468034245]
GateFusion is a novel architecture that combines strong pretrained unimodal encoders with a Hierarchical Gated Fusion Decoder (HiGate)<n>HiGate enables progressive, multi-depth fusion by adaptively injecting contextual features from one modality into the other at multiple layers of the Transformer backbone.
arXiv Detail & Related papers (2025-12-17T18:56:52Z) - StutterFuse: Mitigating Modality Collapse in Stuttering Detection with Jaccard-Weighted Metric Learning and Gated Fusion [0.40105987447353786]
Stuttering detection breaks down when disfluencies overlap.<n>Existing parametric models struggle to distinguish complex, simultaneous disfluencies.<n>We introduce StutterFuse, the first Retrieval-Augmented generalization (RAC) for multi-label detection.
arXiv Detail & Related papers (2025-12-15T18:28:39Z) - RaCoT: Plug-and-Play Contrastive Example Generation Mechanism for Enhanced LLM Reasoning Reliability [12.67288560758937]
We propose RaCoT (Retrieval-aware Contrastive-of-Thought), a novel framework that shifts contrastive thinking to the pre-retrieval stage.<n>RaCoT guides the model to proactively focus on the critical details that determine answer divergence"
arXiv Detail & Related papers (2025-10-26T15:06:44Z) - Few-Step Diffusion via Score identity Distillation [67.07985339442703]
Diffusion distillation has emerged as a promising strategy for accelerating text-to-image (T2I) diffusion models.<n>Existing methods rely on real or teacher-synthesized images to perform well when distilling high-resolution T2I diffusion models.<n>We propose two new guidance strategies: Zero-CFG, which disables CFG in the teacher and removes text conditioning in the fake score network, and Anti-CFG, which applies negative CFG in the fake score network.
arXiv Detail & Related papers (2025-05-19T03:45:16Z) - DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial
Purification [63.65630243675792]
Diffusion-based purification defenses leverage diffusion models to remove crafted perturbations of adversarial examples.
Recent studies show that even advanced attacks cannot break such defenses effectively.
We propose a unified framework DiffAttack to perform effective and efficient attacks against diffusion-based purification defenses.
arXiv Detail & Related papers (2023-10-27T15:17:50Z) - High-Fidelity Speech Synthesis with Minimal Supervision: All Using
Diffusion Models [56.00939852727501]
Minimally-supervised speech synthesis decouples TTS by combining two types of discrete speech representations.
Non-autoregressive framework enhances controllability, and duration diffusion model enables diversified prosodic expression.
arXiv Detail & Related papers (2023-09-27T09:27:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.