论文标题
Voxceleb扬声器识别挑战2020的DKU-Dukeece系统2020
The DKU-DukeECE Systems for VoxCeleb Speaker Recognition Challenge 2020
论文作者
论文摘要
在本文中，我们介绍了DKU-Dukeece团队的Voxceleb演讲者识别挑战（VoxSRC-20）的系统提交。对于轨道1，我们探索具有不同的合并层和客观损失功能的各种最先进的前端提取器。对于轨道3，我们采用了一个迭代框架，用于基于深神经网络（DNN）的自我监督的说话者表示。对于轨道4，我们研究了扬声器诊断的整个系统管道，包括语音活动检测（VAD），均匀分割，扬声器嵌入提取和聚类。
In this paper, we present the system submission for the VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20) by the DKU-DukeECE team. For track 1, we explore various kinds of state-of-the-art front-end extractors with different pooling layers and objective loss functions. For track 3, we employ an iterative framework for self-supervised speaker representation learning based on a deep neural network (DNN). For track 4, we investigate the whole system pipeline for speaker diarization, including voice activity detection (VAD), uniform segmentation, speaker embedding extraction, and clustering.
