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Naive System Report details TdSV Challenge 2024 performance

By PulseAugur Editorial · [1 sources] ·

A research paper details a system developed for the 2024 Text-Dependent Speaker Verification (TdSV) Challenge, achieving a Minimum Detection Cost Function of 0.0461 and an Equal Error Rate of 1.3%. The system adapted existing neural networks like ResNet-TDNN and NeXt-TDNN, originally trained on VoxCeleb, due to time and resource constraints. An EfficientNet-A0 model was also trained on the challenge dataset to enhance adaptation and bolster an ensemble approach, demonstrating the efficacy of multi-model ensembles for speaker and phrase verification. AI

IMPACT This research contributes to advancements in speaker verification technology, potentially improving security and user authentication systems.

RANK_REASON The cluster contains a research paper detailing a system's performance in a specific challenge. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Naive System Report details TdSV Challenge 2024 performance

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The cluster contains a research paper detailing a system's performance in a specific challenge. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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108 days old
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pourya Jafarzadeh ·

    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 netwo…

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