arXiv (All)
open access
ML-ITW: A Multilingual in-the-wild Benchmark for Speech Deepfake Detection
Recent advances in speech synthesis and voice conversion have greatly improved the naturalness and authenticity of generated audio. Meanwhile, evolving encoding, compression, and transmission mechanisms on social media platforms further obscure deepfake artifacts. These factors complicate reliable d
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
- SPADE: A Multilingual Dataset for Speech Partial Deepfake Detection and Localization#1051690
- Not All Attacks Are Learned Equally in Speech Deepfake Detection#999451
- Towards Generalizable Deepfake Detection via Real Distribution Bias Correction#1012153
- Why Do You Say It Like That? A Phoneme-level Framework for Explainable Speech Deepfake Detection#1117504
- VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion#363292
Record · ID 1014627
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