论文标题
Voxceleb扬声器识别挑战2022的DKU-Dukeece诊断系统2022
The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022
论文作者
论文摘要
本文将dku-dukeece提交给Voxceleb扬声器识别挑战2022（VOXSRC-22）的第四轨。我们的系统包含融合的语音活动检测模型，基于聚类的诊断模型以及基于目标语音活动检测的重叠检测模型。总体而言，提交的系统类似于我们上一年的VoxSRC-21系统。不同之处在于，我们使用更好的扬声器嵌入和融合的语音活动检测，从而大大提高了性能。最后，我们使用Dover-lap融合了4个不同的系统，并达到了诊断错误率的4.75，该系统排名第4轨中的第一名。
This paper discribes the DKU-DukeECE submission to the 4th track of the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our system contains a fused voice activity detection model, a clustering-based diarization model, and a target-speaker voice activity detection-based overlap detection model. Overall, the submitted system is similar to our previous year's system in VoxSRC-21. The difference is that we use a much better speaker embedding and a fused voice activity detection, which significantly improves the performance. Finally, we fuse 4 different systems using DOVER-lap and achieve 4.75 of the diarization error rate, which ranks the 1st place in track 4.
