SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection

• URL: http://arxiv.org/abs/2603.20686v1
• Date: Sat, 21 Mar 2026 07:05:30 GMT
• Title: SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection
• Abstract summary: Self-supervised learning-based speech encoders struggle to generalize across unseen speakers.<n>Speaker entanglement causes detectors to exploit speaker-specific correlations rather than artifact-related cues.<n>We introduce SNAP, a speaker-nulling framework, to mitigate this reliance.
• Score: 51.096014381455454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in text-to-speech technologies enable generating high-fidelity synthetic speech nearly indistinguishable from real human voices. While recent studies show the efficacy of self-supervised learning-based speech encoders for deepfake detection, these models struggle to generalize across unseen speakers. Our quantitative analysis suggests these encoder representations are substantially influenced by speaker information, causing detectors to exploit speaker-specific correlations rather than artifact-related cues. We call this phenomenon speaker entanglement. To mitigate this reliance, we introduce SNAP, a speaker-nulling framework. We estimate a speaker subspace and apply orthogonal projection to suppress speaker-dependent components, isolating synthesis artifacts within the residual features. By reducing speaker entanglement, SNAP encourages detectors to focus on artifact-related patterns, leading to state-of-the-art performance.

Related papers

• A Novel Automatic Framework for Speaker Drift Detection in Synthesized Speech [34.33842514204989]
  Recent diffusion-based text-to-speech (TTS) models achieve high naturalness and expressiveness, yet often suffer from speaker drift.<n>We introduce the first automatic framework for detecting speaker drift by formulating it as a binary classification task over utterance-level speaker consistency.<n>Our method computes cosine similarity across overlapping segments of synthesized speech and prompts large language models with structured representations to assess drift.
  arXiv  Detail & Related papers  (2026-04-07T18:05:28Z)
• Identifying Speaker Information in Feed-Forward Layers of Self-Supervised Speech Transformers [50.9040167152168]
  We analyze neurons associated with k-means clusters of self-supervised features and i-vectors.<n>Our analysis reveals that these clusters correspond to broad phonetic and gender classes.<n>By protecting these neurons during pruning, we can significantly preserve performance on speaker-related task.
  arXiv  Detail & Related papers  (2025-06-26T18:54:26Z)
• Generalizable Zero-Shot Speaker Adaptive Speech Synthesis with Disentangled Representations [12.388567657230116]
  We propose a generalizable zero-shot speaker adaptive text-to-speech and voice conversion model. GZS-TV introduces disentangled representation learning for speaker embedding extraction and timbre transformation. Our experiments demonstrate that GZS-TV reduces performance degradation on unseen speakers and outperforms all baseline models in multiple datasets.
  arXiv  Detail & Related papers  (2023-08-24T18:13:10Z)
• Combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection [15.884911752869437]
  We present a novel approach for synthetic speech detection, exploiting the combination of two high-level semantic properties of the human voice. On one side, we focus on speaker identity cues and represent them as speaker embeddings extracted using a state-of-the-art method for the automatic speaker verification task. On the other side, voice prosody, intended as variations in rhythm, pitch or accent in speech, is extracted through a specialized encoder.
  arXiv  Detail & Related papers  (2022-10-31T11:03:03Z)
• In search of strong embedding extractors for speaker diarisation [49.7017388682077]
  We tackle two key problems when adopting EEs for speaker diarisation. First, the evaluation is not straightforward because the features required for better performance differ between speaker verification and diarisation. We show that better performance on widely adopted speaker verification evaluation protocols does not lead to better diarisation performance. We propose two data augmentation techniques to alleviate the second problem, making embedding extractors aware of overlapped speech or speaker change input.
  arXiv  Detail & Related papers  (2022-10-26T13:00:29Z)
• Deepfake audio detection by speaker verification [79.99653758293277]
  We propose a new detection approach that leverages only the biometric characteristics of the speaker, with no reference to specific manipulations. The proposed approach can be implemented based on off-the-shelf speaker verification tools. We test several such solutions on three popular test sets, obtaining good performance, high generalization ability, and high robustness to audio impairment.
  arXiv  Detail & Related papers  (2022-09-28T13:46:29Z)
• Disentangled dimensionality reduction for noise-robust speaker diarisation [30.383712356205084]
  Speaker embeddings play a crucial role in the performance of diarisation systems. Speaker embeddings often capture spurious information such as noise and reverberation, adversely affecting performance. We propose a novel dimensionality reduction framework that can disentangle spurious information from the speaker embeddings. We also propose the use of a speech/non-speech indicator to prevent the speaker code from learning from the background noise.
  arXiv  Detail & Related papers  (2021-10-07T12:19:09Z)
• Active Speakers in Context [88.22935329360618]
  Current methods for active speak er detection focus on modeling short-term audiovisual information from a single speaker. This paper introduces the Active Speaker Context, a novel representation that models relationships between multiple speakers over long time horizons. Our experiments show that a structured feature ensemble already benefits the active speaker detection performance.
  arXiv  Detail & Related papers  (2020-05-20T01:14:23Z)
• Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention [70.82604384963679]
  This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features. We extract a speaker representation used for adaptation directly from the test utterance.
  arXiv  Detail & Related papers  (2020-02-14T05:05:36Z)

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.