arXiv (OAI Expanded)
open access
Language Orthogonalization for Zero-Shot Cross-Lingual Audio Deepfake Detection
Audio deepfake detectors need to transfer to languages absent from training, as multilingual speech synthesis outpaces labeled anti-spoofing resources. While detectors increasingly rely on self-supervised speech models (S3Ms), these backbones encode language-dependent structure that confounds spoof
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision#1144646
- MLAAD: The Multi-Language Audio Anti-Spoofing Dataset#785033
- MLAAD: The Multi-Language Audio Anti-Spoofing Dataset#783443
- DriftAudio: Marginal Drifting for Distributional Post-Training of One-Step Text-to-Audio Generators#1064142
- Is Broader Better? A Controlled Study of Multilingual Coverage and Pretraining Objective in Frozen SSL Encoders for Speech Deepfake Detection#1077067
Record · ID 1122503
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive