DeepAI AI Chat
Log In Sign Up

The ins and outs of speaker recognition: lessons from VoxSRC 2020

by   Yoohwan Kwon, et al.

The VoxCeleb Speaker Recognition Challenge (VoxSRC) at Interspeech 2020 offers a challenging evaluation for speaker recognition systems, which includes celebrities playing different parts in movies. The goal of this work is robust speaker recognition of utterances recorded in these challenging environments. We utilise variants of the popular ResNet architecture for speaker recognition and perform extensive experiments using a range of loss functions and training parameters. To this end, we optimise an efficient training framework that allows powerful models to be trained with limited time and resources. Our trained models demonstrate improvements over most existing works with lighter models and a simple pipeline. The paper shares the lessons learned from our participation in the challenge.


page 1

page 2

page 3

page 4


Clova Baseline System for the VoxCeleb Speaker Recognition Challenge 2020

This report describes our submission to the VoxCeleb Speaker Recognition...

Tongji University Team for the VoxCeleb Speaker Recognition Challenge 2020

In this report, we describe the submission of Tongji University team to ...

Beijing ZKJ-NPU Speaker Verification System for VoxCeleb Speaker Recognition Challenge 2021

In this report, we describe the Beijing ZKJ-NPU team submission to the V...

Disentangled representation learning for multilingual speaker recognition

The goal of this paper is to train speaker embeddings that are robust to...

Unified Hypersphere Embedding for Speaker Recognition

Incremental improvements in accuracy of Convolutional Neural Networks ar...

Delving into VoxCeleb: environment invariant speaker recognition

Research in speaker recognition has recently seen significant progress d...

Utterance-level Aggregation For Speaker Recognition In The Wild

The objective of this paper is speaker recognition "in the wild"-where u...