Clova Baseline System for the VoxCeleb Speaker Recognition Challenge 2020

Abstract

This report describes our submission to the VoxCeleb Speaker Recognition Challenge (VoxSRC) at Interspeech 2020. We perform a careful analysis of speaker recognition models based on the popular ResNet architecture, and train a number of variants using a range of loss functions. Our results show significant improvements over most existing works without the use of model ensemble or post-processing. We release the training code and pre-trained models as unofficial baselines for this year's challenge.

References in corpus (4)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• BUT System Description to VoxCeleb Speaker Recognition Challenge 2019
• NSML: A Machine Learning Platform That Enables You to Focus on Your Models
• VoxSRC 2019: The first VoxCeleb Speaker Recognition Challenge

Cited by in corpus (5)

• Sparsely Overlapped Speech Training in the Time Domain: Joint Learning of Target Speech Separation and Personal VAD Benefits
• ShaneRun System Description to VoxCeleb Speaker Recognition Challenge 2020
• Generalized Operating Procedure for Deep Learning: an Unconstrained Optimal Design Perspective
• Duality Temporal-channel-frequency Attention Enhanced Speaker Representation Learning
• Multi-Level Transfer Learning from Near-Field to Far-Field Speaker Verification