论文标题
Voxceleb扬声器识别挑战2020的Clova基线系统2020
Clova Baseline System for the VoxCeleb Speaker Recognition Challenge 2020
论文作者
论文摘要
该报告描述了我们对Interspeech 2020上Voxceleb扬声器识别挑战（VOXSRC）的提交。我们对基于流行的Resnet体系结构的说话者识别模型进行了仔细的分析，并使用一系列损失功能训练许多变体。我们的结果表明，在不使用模型集合或后处理的情况下，大多数现有作品都有显着改善。我们将培训代码和预培训模型作为今年挑战的非官方基准。
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.
