Faculty of Information Technology, BUT

Publication Details

Language Recognition in iVectors Space

MARTÍNEZ González David, PLCHOT Oldřich, BURGET Lukáš, GLEMBEK Ondřej and MATĚJKA Pavel. Language Recognition in iVectors Space. In: Proceedings of Interspeech 2011. Florence: International Speech Communication Association, 2011, pp. 861-864. ISBN 978-1-61839-270-1. ISSN 1990-9772.
Czech title
Rozpoznávání jazyka v prostoru iVektorů
Type
conference paper
Language
english
Authors
Martínez González David (UNIZAR)
Plchot Oldřich, Ing., Ph.D. (DCGM FIT BUT)
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT)
Glembek Ondřej, Ing., Ph.D. (DCGM FIT BUT)
Matějka Pavel, Ing., Ph.D. (DCGM FIT BUT)
URL
Keywords
Acoustic Language Recognition, iVectors, Joint Factor Analysis
Abstract
We have introduced a novel approach for language recognition. Three classifiers (linear generative model, SVM and logistic regression) have been tested in the iVector space, and all outperform the state-of-the-art JFA system. Very simple and fast classifier based on linear generative model provides excellent performance over all conditions. The advantage of this classifier is also its scalability: addition of a new language only requires estimating the mean over the corresponding iVectors. Most of the computational load is in the iVector generation. Hence, as a next step, we will try to obtain iVectors from the utterances and the corresponding sufficient statistics in a more direct way.
Annotation
The concept of so called iVectors, where each utterance is represented by fixed-length low-dimensional feature vector, has recently become very successfully in speaker verification. In this work, we apply the same idea in the context of Language Recognition (LR). To recognize language in the iVector space, we experiment with three different linear classifiers: one based on a generative model, where classes are modeled by Gaussian distributions with shared covariance matrix, and two discriminative classifiers, namely linear Support Vector Machine and Logistic Regression. The tests were performed on the NIST LRE 2009 dataset and the results were compared with stateof- the-art LR based on Joint Factor Analysis (JFA). While the iVector system offers better performance, it also seems to be complementary to JFA, as their fusion shows another improvement.
Published
2011
Pages
861-864
Journal
Proceedings of Interspeech, vol. 2011, no. 8, ISSN 1990-9772
Proceedings
Proceedings of Interspeech 2011
Conference
Interspeech 2011, Florence Italy, IT
ISBN
978-1-61839-270-1
Publisher
International Speech Communication Association
Place
Florence, IT
BibTeX
@INPROCEEDINGS{FITPUB9754,
   author = "David Gonz\'{a}lez Mart\'{i}nez and Old\v{r}ich Plchot and Luk\'{a}\v{s} Burget and Ond\v{r}ej Glembek and Pavel Mat\v{e}jka",
   title = "Language Recognition in iVectors Space",
   pages = "861--864",
   booktitle = "Proceedings of Interspeech 2011",
   journal = "Proceedings of Interspeech",
   volume = 2011,
   number = 8,
   year = 2011,
   location = "Florence, IT",
   publisher = "International Speech Communication Association",
   ISBN = "978-1-61839-270-1",
   ISSN = "1990-9772",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/9754"
}
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