Recently we have introduced a method named inter-dataset variability compensation (IDVC) in the context of speaker recognition in a mismatched dataset. IDVC compensates dataset shifts in the i-vector space by constraining the shifts to a low dimensional subspace. The subspace is estimated from a heterogeneous development set which is partitioned into homogenous subsets. In this work we generalize the IDVC method to compensate inter-dataset variability attributed to additional PLDA hyper-parameters, namely the within and between speaker covariance matrices. Using the proposed method we managed to recover 85% of the degradation due to mismatched PLDA training in the framework of the JHU-2013 domain adaptation challenge.
Cite as: Aronowitz, H. (2014) Compensating Inter-Dataset Variability in PLDA Hyper-Parameters for Robust Speaker Recognition. Proc. The Speaker and Language Recognition Workshop (Odyssey 2014), 280-286, doi: 10.21437/Odyssey.2014-42
@inproceedings{aronowitz14_odyssey,
author={Hagai Aronowitz},
title={{Compensating Inter-Dataset Variability in PLDA Hyper-Parameters for Robust Speaker Recognition}},
year=2014,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2014)},
pages={280--286},
doi={10.21437/Odyssey.2014-42}
}