论文标题
Voxceleb扬声器诊断挑战2020的EML系统描述2020
EML System Description for VoxCeleb Speaker Diarization Challenge 2020
论文作者
论文摘要
该技术报告描述了对第一个Voxceleb发言人诊断挑战的EML提交。尽管挑战的目的是信号的离线处理，但提交的系统基本上是EML在线算法，该算法在大约每1.2秒的运行时决定扬声器标签。对于挑战的第一阶段，仅使用Voxceleb2开发数据集进行培训。与挑战中提供的离线基线相比，提供的VoxConverse Dev集合的结果在DER和JER方面都表现出更好的准确性。整个诊断过程的实时因子使用一台CPU机器约为0.01。
This technical report describes the EML submission to the first VoxCeleb speaker diarization challenge. Although the aim of the challenge has been the offline processing of the signals, the submitted system is basically the EML online algorithm which decides about the speaker labels in runtime approximately every 1.2 sec. For the first phase of the challenge, only VoxCeleb2 dev dataset was used for training. The results on the provided VoxConverse dev set show much better accuracy in terms of both DER and JER compared to the offline baseline provided in the challenge. The real-time factor of the whole diarization process is about 0.01 using a single CPU machine.
