In this paper, we explore new high-level features for language identification. The recently introduced Subspace Gaussian Mixture Models (SGMM) provide an elegant and efficient way for GMM acoustic modelling, with mean supervectors represented in a low-dimensional representative subspace. SGMMs also provide an efficient way of speaker adaptation by means of lowdimensional vectors. In our framework, these vectors are used as features for language identification. They are compared with our acoustic iVector system, which architecture is currently considered state-of-the-art for Language Identification and Speaker Verification. The results of both systems and their fusion are reported on the NIST LRE2009 dataset.
Cite as: Plchot, O., Karafiát, M., Brümmer, N., Glembek, O., Matějka, P., Villiers, E.d., Černocký, J.". (2012) Speaker vectors from subspace Gaussian mixture model as complementary features for language identification. Proc. The Speaker and Language Recognition Workshop (Odyssey 2012), 330-333
@inproceedings{plchot12_odyssey,
author={Oldřich Plchot and Martin Karafiát and Niko Brümmer and Ondřej Glembek and Pavel Matějka and Edward de Villiers and Jan "Honza" Černocký},
title={{Speaker vectors from subspace Gaussian mixture model as complementary features for language identification}},
year=2012,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2012)},
pages={330--333}
}