# Text-Dependent Speaker Verification (TdSV) Challenge 2024: Team Naive System Report

arXiv: https://arxiv.org/abs/2605.14896 · HTML: https://mlsanity.com/paper/2605.14896/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 14 May 2026.

## Builds on (most cited first, 5 of 5)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: the DeepMine Database](https://mlsanity.com/paper/1912.03627/) (2019)
- [End-to-End Attention based Text-Dependent Speaker Verification](https://mlsanity.com/paper/1701.00562/) (2017)
- [NeXt-TDNN: Modernizing Multi-Scale Temporal Convolution Backbone for Speaker Verification](https://mlsanity.com/paper/2312.08603/) (2023)
- [Text-dependent Speaker Verification (TdSV) Challenge 2024: Challenge Evaluation Plan](https://mlsanity.com/paper/2404.13428/) (2024)

## Abstract (arXiv)

This paper presents a system for the 2024 Text-Dependent Speaker Verification (TdSV) Challenge. The system achieved a Minimum Detection Cost Function (MinDCF) of 0.0461 and an Equal Error Rate (EER) of 1.3%. Our approach focused on adapting existing state-of-the-art neural networks, ResNet-TDNN and NeXt-TDNN, originally trained on the VoxCeleb dataset. This strategy was chosen because of the limited challenge duration and the available resources at the time. In addition, we designed a lightweight and resource-efficient model, EfficientNet-A0, trained specifically on the challenge dataset to improve adaptation and strengthen the ensemble approach. Our system combines advanced neural architectures, extensive data augmentation, and optimised hyperparameters. These components helped achieve strong performance in text-dependent speaker verification. The results also demonstrate the effectiveness of multi-model ensemble learning for both speaker and phrase verification.