# Short-duration Speaker Verification (SdSV) Challenge 2021: the Challenge Evaluation Plan

arXiv: https://arxiv.org/abs/1912.06311 · HTML: https://mlsanity.com/paper/1912.06311/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 9 of 9)

- [Bias in Automated Speaker Recognition](https://mlsanity.com/paper/2201.09486/) (2022)
- [SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions](https://mlsanity.com/paper/2509.17091/) (2025)
- [Enrollment-stage Backdoor Attacks on Speaker Recognition Systems via Adversarial Ultrasound](https://mlsanity.com/paper/2306.16022/) (2023)
- [SVEva Fair: A Framework for Evaluating Fairness in Speaker Verification](https://mlsanity.com/paper/2107.12049/) (2021)
- [CS-Rep: Making Speaker Verification Networks Embracing Re-parameterization](https://mlsanity.com/paper/2110.13465/) (2021)
- [Class Token and Knowledge Distillation for Multi-head Self-Attention Speaker Verification Systems](https://mlsanity.com/paper/2111.03842/) (2021)
- [POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan](https://mlsanity.com/paper/2603.24569/) (2026)
- [Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification](https://mlsanity.com/paper/2102.02074/) (2021)
- [On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification](https://mlsanity.com/paper/2201.06426/) (2022)

## Abstract (arXiv)

This document describes the Short-duration Speaker Verification (SdSV) Challenge 2021. The main goal of the challenge is to evaluate new technologies for text-dependent (TD) and text-independent (TI) speaker verification (SV) in a short duration scenario. The proposed challenge evaluates SdSV with varying degree of phonetic overlap between the enrollment and test utterances (cross-lingual). It is the first challenge with a broad focus on systematic benchmark and analysis on varying degrees of phonetic variability on short-duration speaker recognition. We expect that modern methods (deep neural networks in particular) will play a key role.