Text-Dependent Speaker Verification (TdSV) Challenge 2024: Team Naive System Report
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 archi...
| Authors | Amir Mohammad Rostami, Pourya Jafarzadeh |
|---|---|
| Primary category | cs.SD |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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