Publication Details
Speaker vectors from Subspace Gaussian Mixture Model as complementary features for Language Identification
Karafiát Martin, Ing., Ph.D. (DCGM FIT BUT)
Brummer Johan Nikolaas Langenhoven (Agnitio)
Glembek Ondřej, Ing., Ph.D. (DCGM FIT BUT)
Matějka Pavel, Ing., Ph.D. (DCGM FIT BUT)
de Villiers Edward (Agnitio)
Černocký Jan, prof. Dr. Ing. (DCGM FIT BUT)
speaker recognition, Gaussian Mixture Model, speaker vectors, language identification
In this paper we have presented new features for language identification, based on speaker adaptation vectors from sub-space Gaussian Mixture Models.
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.
@INPROCEEDINGS{FITPUB10056,
author = "Old\v{r}ich Plchot and Martin Karafi\'{a}t and Langenhoven Nikolaas Johan Brummer and Ond\v{r}ej Glembek and Pavel Mat\v{e}jka and Edward Villiers de and Jan \v{C}ernock\'{y}",
title = "Speaker vectors from Subspace Gaussian Mixture Model as complementary features for Language Identification",
pages = "330--333",
booktitle = "Proceedings of Odyssey 2012, The Speaker and Language Recognition Workshop",
year = 2012,
location = "Singapur, SG",
publisher = "International Speech Communication Association",
ISBN = "978-981-07-3093-2",
language = "english",
url = "https://www.fit.vut.cz/research/publication/10056"
}