In this paper, we introduce a new subspace learning algorithm in language recognition called locality preserving discriminant projection (LPDP). Total variability approach has been the state of art in language recognition, and it preserves most of the discriminant information of languages. Locality preserving projection (LPP) has been proved effective in language recognition, but it can only preserve the local structure of languages. LPDP method used in the total variability subspace can preserve both local structure and global discriminant information about the languages. Experiments are carried out on NIST 2011 Language Recognition Evaluation (LRE) database. The results indicate that LPDP language recognition system performs better than LPP language recognition system and total variability language recognition system in 30 s tasks. In addition, we also give the results of the total variability and LPDP language recognition systems on NIST 2007 LRE 30 s database.
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