# ASVspoof 2019: spoofing countermeasures for the detection of synthesized, converted and replayed speech

arXiv: https://arxiv.org/abs/2102.05889 · HTML: https://mlsanity.com/paper/2102.05889/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2021.

## Built on by (most cited first, 30 of 58)

- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [Does Audio Deepfake Detection Generalize?](https://mlsanity.com/paper/2203.16263/) (2022)
- [WaveFake: A Data Set to Facilitate Audio Deepfake Detection](https://mlsanity.com/paper/2111.02813/) (2021)
- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment](https://mlsanity.com/paper/2507.02768/) (2025)
- [SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks](https://mlsanity.com/paper/2303.00733/) (2023)
- [The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio](https://mlsanity.com/paper/2405.04880/) (2024)
- [XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection](https://mlsanity.com/paper/2411.10027/) (2024)
- [SafeEar: Content Privacy-Preserving Audio Deepfake Detection](https://mlsanity.com/paper/2409.09272/) (2024)
- [DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset](https://mlsanity.com/paper/2409.08731/) (2024)
- [Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks](https://mlsanity.com/paper/2107.08803/) (2021)
- [SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing](https://mlsanity.com/paper/2211.02718/) (2022)
- [Attentive Merging of Hidden Embeddings from Pre-trained Speech Model for Anti-spoofing Detection](https://mlsanity.com/paper/2406.10283/) (2024)
- [ALLM4ADD: Unlocking the Capabilities of Audio Large Language Models for Audio Deepfake Detection](https://mlsanity.com/paper/2505.11079/) (2025)
- [Learning From Yourself: A Self-Distillation Method for Fake Speech Detection](https://mlsanity.com/paper/2303.01211/) (2023)
- [Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection](https://mlsanity.com/paper/2308.03300/) (2023)
- [Where are we in audio deepfake detection? A systematic analysis over generative and detection models](https://mlsanity.com/paper/2410.04324/) (2024)
- [An Efficient Temporary Deepfake Location Approach Based Embeddings for Partially Spoofed Audio Detection](https://mlsanity.com/paper/2309.03036/) (2023)
- [Topological Data Analysis for Speech Processing](https://mlsanity.com/paper/2211.17223/) (2022)
- [UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions](https://mlsanity.com/paper/2310.02973/) (2023)
- [FairSSD: Understanding Bias in Synthetic Speech Detectors](https://mlsanity.com/paper/2404.10989/) (2024)
- [Complex-valued neural networks for voice anti-spoofing](https://mlsanity.com/paper/2308.11800/) (2023)
- [Codecfake: An Initial Dataset for Detecting LLM-based Deepfake Audio](https://mlsanity.com/paper/2406.08112/) (2024)
- [SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection](https://mlsanity.com/paper/2211.06073/) (2022)
- [Range-Based Equal Error Rate for Spoof Localization](https://mlsanity.com/paper/2305.17739/) (2023)
- [Generalized Source Tracing: Detecting Novel Audio Deepfake Algorithm with Real Emphasis and Fake Dispersion Strategy](https://mlsanity.com/paper/2406.03240/) (2024)
- [Generalizing Speaker Verification for Spoof Awareness in the Embedding Space](https://mlsanity.com/paper/2401.11156/) (2024)
- [Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio](https://mlsanity.com/paper/2406.07816/) (2024)
- [AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan](https://mlsanity.com/paper/2604.08184/) (2026)
- [Spoofing-Aware Speaker Verification with Unsupervised Domain Adaptation](https://mlsanity.com/paper/2203.10992/) (2022)

## Abstract (arXiv)

The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV). This paper describes the third in a series of bi-annual challenges: ASVspoof 2019. With the challenge database and protocols being described elsewhere, the focus of this paper is on results and the top performing single and ensemble system submissions from 62 teams, all of which out-perform the two baseline systems, often by a substantial margin. Deeper analyses shows that performance is dominated by specific conditions involving either specific spoofing attacks or specific acoustic environments. While fusion is shown to be particularly effective for the logical access scenario involving speech synthesis and voice conversion attacks, participants largely struggled to apply fusion successfully for the physical access scenario involving simulated replay attacks. This is likely the result of a lack of system complementarity, while oracle fusion experiments show clear potential to improve performance. Furthermore, while results for simulated data are promising, experiments with real replay data show a substantial gap, most likely due to the presence of additive noise in the latter. This finding, among others, leads to a number of ideas for further research and directions for future editions of the ASVspoof challenge.