Jhu-HLTCOE System for the Voxsrc Speaker Recognition Challenge
Daniel Garcia-Romero, Alan McCree, David Snyder, Gregory Sell
Abstract
The VoxSRC speaker recognition challenge comprises data obtained from YouTube videos of celebrity interviews in a wide range of recording environments. The challenge provides FIXED and OPEN training conditions to allow cross-system comparisons and to characterize the effects of additional amounts of training data on system performance. This paper describes our submission to this challenge where we have explored x-vector extractor topologies, classification head alternatives, data augmentation, and angular margin penalty. Our final entry to the FIXED condition (which achieved 2nd place) is the score average of 4 diverse systems. We find that this system outperforms a large single DNN with similar number of parameters.
BibTeX
@inproceedings{icassp2020_jhuhltcoesystemf,
title = {Jhu-HLTCOE System for the Voxsrc Speaker Recognition Challenge},
author = {Daniel Garcia-Romero and Alan McCree and David Snyder and Gregory Sell},
booktitle = {ICASSP 2020},
year = {2020}
}