VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion
Modern LLM-driven text-to-speech (TTS) and voice conversion (VC) systems produce synthetic speech that differs from the generators represented in many legacy spoofing benchmarks. This mismatch creates a temporal generalization gap that can overestimate detector robustness under real-world post-processing conditions. We bridge this gap by introducing VoxENES 2026, a bilingual (English and Spanish) benchmark of 53,628 audio samples generated using 10 contemporary speech synthesis methods and evaluated under 10 standardized post-processing conditions. Using VoxENES 2026, we benchmark eight pretra
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- PossiblePossibly related (embedding) · 52%Kyutai's Pocket TTS clones a voice from 5 seconds of audio, on CPU, under MIT. Benchmarked against Kokoro, Supertonic, and Inflect-Nano for Eng. TTS →
- PossiblePossibly related (embedding) · 52%app-vox/vox →
- PossiblePossibly related (embedding) · 49%lgy1027/matrix-live-diarizer →
- PossiblePossibly related (embedding) · 46%Apple's new SpeechAnalyzer API, benchmarked against Whisper and its predecessor →
- LinkedLinked via arxiv author · 85%Aastha Sharma →
“VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion”
- LinkedLinked via arxiv author · 85%Guangjing Wang →
“VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion”
- FuzzySimilar title/name (fuzzy) · 87%huggingface/speech-to-speech →
“Fuzzy title match (0.94): “VoxENES 2026: Benchmarking Generalization of Speech Spoofing” ≈ “huggingface/speech-to-speech””
- FuzzyOverlapping authors or contributors · 62%ultralytics/yolov5 →
“Shared author/contributor keys: sharma”