arXiv:2605. 29987v2 Announce Type: replace Abstract: Although multi-scales representation learning enables elastic-dimension embeddings, nested subspaces often suffer from dimensional redundancy and spectral collapse.
arXiv:2407.04291v4 Announce Type: replace-cross Abstract: Modeling speech variation is key to natural, expressive generation. Speaker embeddings are commonly used to condition personalized speech sys...
arXiv:2606. 11915v1 Announce Type: cross Abstract: We present a quality-adaptive angular-margin learning framework that improves feature generalization by enforcing intra-class compactness and inter-class separability.
arXiv:2603. 21875v2 Announce Type: replace-cross Abstract: Speech deepfake source verification systems aims to determine whether two synthetic speech utterances originate from the same source generator, often assuming that the resulting source embeddings are independent of speaker traits.
arXiv:2512. 10120v2 Announce Type: replace-cross Abstract: General-purpose audio representations aim to map acoustically variable instances of the same event to nearby points, resolving content identity in a zero-shot setting.
arXiv:2609.38887v1 Announce Type: cross Abstract: Real-time voice conversion (VC) systems commonly rely on pretrained speaker embeddings from automatic speaker verification (ASV) models. While effect...
arXiv:2603. 10827v2 Announce Type: replace-cross Abstract: Speech-aware large language models (LLMs) can accept speech inputs, yet their training objectives largely emphasize linguistic content or specific fields such as emotions or the speaker's gender, leaving it unclear whether they encode speaker identity.
arXiv:2609.25007v1 Announce Type: cross Abstract: The performance of state-of-the-art speaker verification (SV) systems severely degrades on short utterances due to insufficient speaker-specific info...
arXiv:2606. 08843v1 Announce Type: cross Abstract: We present a voice conversion (VC) framework that utilizes K-Nearest Neighbors (KNN) retrieval over WavLM representations to align non-parallel source and target speech, constructing synthetic training pairs for supervised learning.
arXiv:2606. 27543v1 Announce Type: cross Abstract: The variations in vocal effort range (e.
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.
arXiv:2606. 31552v1 Announce Type: cross Abstract: Room-acoustic simulations are widely used to augment training data for deep-learning-based speech enhancement.