# SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions

arXiv: https://arxiv.org/abs/2509.17091 · HTML: https://mlsanity.com/paper/2509.17091/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 56 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 25 of 25)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens](https://mlsanity.com/paper/2407.05407/) (2024)
- [Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems](https://mlsanity.com/paper/1911.01840/) (2019)
- [EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation](https://mlsanity.com/paper/2406.06185/) (2024)
- [Did you hear that? Adversarial Examples Against Automatic Speech Recognition](https://mlsanity.com/paper/1801.00554/) (2018)
- [MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification](https://mlsanity.com/paper/2203.15249/) (2022)
- [IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages](https://mlsanity.com/paper/2208.11761/) (2022)
- [Reshape Dimensions Network for Speaker Recognition](https://mlsanity.com/paper/2407.18223/) (2024)
- [VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2012.06867/) (2020)
- [ECAPA2: A Hybrid Neural Network Architecture and Training Strategy for Robust Speaker Embeddings](https://mlsanity.com/paper/2401.08342/) (2024)
- [Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems](https://mlsanity.com/paper/2008.07685/) (2020)
- [Speech Robust Bench: A Robustness Benchmark For Speech Recognition](https://mlsanity.com/paper/2403.07937/) (2024)
- [Semi-Supervised Contrastive Learning with Generalized Contrastive Loss and Its Application to Speaker Recognition](https://mlsanity.com/paper/2006.04326/) (2020)
- [The INTERSPEECH 2020 Far-Field Speaker Verification Challenge](https://mlsanity.com/paper/2005.08046/) (2020)
- [The 2021 NIST Speaker Recognition Evaluation](https://mlsanity.com/paper/2204.10242/) (2022)
- [Short-duration Speaker Verification (SdSV) Challenge 2021: the Challenge Evaluation Plan](https://mlsanity.com/paper/1912.06311/) (2019)
- [VoxBlink: A Large Scale Speaker Verification Dataset on Camera](https://mlsanity.com/paper/2308.07056/) (2023)
- [MultiSV: Dataset for Far-Field Multi-Channel Speaker Verification](https://mlsanity.com/paper/2111.06458/) (2021)
- [A Study on Bias and Fairness In Deep Speaker Recognition](https://mlsanity.com/paper/2303.08026/) (2023)
- [Disentangled Representation Learning for Environment-agnostic Speaker Recognition](https://mlsanity.com/paper/2406.14559/) (2024)
- [From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification](https://mlsanity.com/paper/2505.04629/) (2025)

## Built on by (most cited first, 6 of 6)

- [DELULU: Discriminative Embedding Learning Using Latent Units for Speaker-Aware Self-Trained Speech Foundational Model](https://mlsanity.com/paper/2510.17662/) (2025)
- [What and When to Learn: CURriculum Ranking Loss for Large-Scale Speaker Verification](https://mlsanity.com/paper/2603.24432/) (2026)
- [POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan](https://mlsanity.com/paper/2603.24569/) (2026)
- [The Voiceprint Fallacy: Why Voices Are Not Unique Biometric Imprints](https://mlsanity.com/paper/2608.07980/) (2026)
- [Inquesto Score: A reliability Protocol For Voice Agents](https://mlsanity.com/paper/2609.30514/) (2026)
- [InterBias-SV: Compound Conditions in Speaker Verification](https://mlsanity.com/paper/2609.36500/) (2026)

## Abstract (arXiv)

Speaker verification (SV) models are increasingly integrated into security, personalization, and access control systems, yet their robustness to many real-world challenges remains inadequately benchmarked. These include a variety of natural and maliciously created conditions causing signal degradations or mismatches between enrollment and test data, impacting performance. Existing benchmarks evaluate only subsets of these conditions, missing others entirely. We introduce SVeritas, a comprehensive Speaker Verification tasks benchmark suite, assessing SV systems under stressors like recording duration, spontaneity, content, noise, microphone distance, reverberation, channel mismatches, audio bandwidth, codecs, speaker age, and susceptibility to spoofing and adversarial attacks. While several benchmarks do exist that each cover some of these issues, SVeritas is the first comprehensive evaluation that not only includes all of these, but also several other entirely new, but nonetheless important, real-life conditions that have not previously been benchmarked. We use SVeritas to evaluate several state-of-the-art SV models and observe that while some architectures maintain stability under common distortions, they suffer substantial performance degradation in scenarios involving cross-language trials, age mismatches, and codec-induced compression. Extending our analysis across demographic subgroups, we further identify disparities in robustness across age groups, gender, and linguistic backgrounds. By standardizing evaluation under realistic and synthetic stress conditions, SVeritas enables precise diagnosis of model weaknesses and establishes a foundation for advancing equitable and reliable speaker verification systems.