VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Aastha Sharma, Guangjing Wang
• Venue: arXiv: cs.SD
• Published: 2026-07-13
• Collected: 2026-07-14
• URL: http://arxiv.org/abs/2607.11706v1
• PDF: https://arxiv.org/pdf/2607.11706v1
• DOI: UNKNOWN

Plain-Language Brief

The paper introduces VoxENES 2026, a new benchmark for evaluating the performance of speech spoofing detectors against modern text-to-speech (TTS) and voice conversion (VC) systems. The benchmark contains 53,628 audio samples in English and Spanish generated using 10 contemporary speech synthesis methods and evaluated under 10 standardized post-processing conditions. Eight pretrained detectors were tested without fine-tuning and showed significant performance degradation, with the best model achieving 28.98% Equal Error Rate (EER) overall.

What the Document Says

• VoxENES 2026 is a new benchmark for speech spoofing detectors.
• It is bilingual and contains 53,628 audio samples.
• The samples are generated using 10 contemporary speech synthesis methods.
• The benchmark evaluates the performance under 10 standardized post-processing conditions.
• Eight pretrained detectors were tested without fine-tuning on VoxENES 2026.
• Significant performance degradation was observed.
• The best model achieved 28.98% EER overall.

Key Concepts Mentioned

• VoxENES 2026
• speech spoofing detectors
• text-to-speech (TTS)
• voice conversion (VC)
• audio samples
• contemporary speech synthesis methods
• post-processing conditions
• pretrained detectors
• Equal Error Rate (EER)

Claims Made by the Document

• The benchmark bridges a temporal generalization gap in spoofing detector evaluation.
• The results highlight the reliance on brittle artifacts in current detectors.

Evidence Used

• Introduction and conclusion of the paper

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

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 pretrained detectors without fine-tuning and observe substantial performance degradation: the best model achieves 28.98% EER overall, while most perform near or below random chance across modern generators and perturbations. Our results highlight the reliance on brittle artifacts in current detectors and establish VoxENES 2026 as a practical testbed for developing robust audio spoofing countermeasures.