The Prospect of the Russian Language in Georgia. Insights from the Educated Youth

After the collapse of the Soviet Union, the status of the Russian language in the new-born Republics became a central issue. In the Southern Caucasus, all the Constitutions promulgated by the three Republics opted for ethnocentric language policies that accepted the titular language as the only State Language. However, the role of the Russian language as a lingua franca remained crucial for international communication and everyday interaction. It followed that it continued to play an important role also in education. The present study focuses on Georgia, where a strong derussification policy has taken place in the last decades and aims at understanding to what extent the use of Russian among the young generations has contracted. In particular, we present an analysis conducted on data collected via (i) a survey for young people consisting of questions on their sociolinguistic background and a proficiency test in Russian, and (ii) semi-structured interviews for teachers of Russian and English as Foreign Languages on the research topics

Author manuscript, published in "Eusipco (2013)" COMBINATION OF SVM AND LARGE MARGIN GMM MODELING FOR SPEAKER IDENTIFICATION

Most state-of-the-art speaker recognition systems are partially or completely based on Gaussian mixture models (GMM). GMM have been widely and successfully used in speaker recognition during the last decades. They are traditionally estimated from a world model using the generative criterion of Maximum A Posteriori. In an earlier work, we proposed an efficient algorithm for discriminative learning of GMM with diagonal covariances under a large margin criterion. In this paper, we evaluate the combination of the large margin GMM modeling approach with SVM in the setting of speaker identification. We carry out a full NIST speaker identification task using NIST-SRE’2006 data, in a Symmetrical Factor Analysis compensation scheme. The results show that the two modeling approaches are complementary and that their combination outperforms their single use. Index Terms — Large margin training, Gaussian mixture models, discriminative learning, Support vector machines, speaker recognition. 1