One of the most popular and better performing approaches to language recognition (LR) is Parallel Phonetic Recognition followed by Language Modeling (PPRLM). In this paper we report several improvements in our PPRLM system that allowed us to move from an Equal Error Rate (EER) of over 15% to less than 8% on NIST LR Evaluation 2005 data still using a standard PPRLM system. The most successful improvement was the retraining of the phonetic decoders on larger and more appropriate corpora. We have also developed a new system based on Support Vector Machines (SVMs) that uses as features both Mel Frequency Cepstral Coefficients (MFCCs) and Shifted Delta Cepstra (SDC). This new SVM system alone gives an EER of 10.5% on NIST LRE 2005 data. Fusing our PPRLM system and the new SVM system we achieve an EER of 5.43% on NIST LRE 2005 data, a relative reduction of almost 66% from our baseline system.
Bibliographic reference. Toledano, Doroteo T. / Gonzalez-Dominguez, Javier / Abejon-Gonzalez, Alejandro / Spada, Danilo / Mateos-Garcia, Ismael / Gonzalez-Rodriguez, Joaquin (2007): "Improved language recognition using better phonetic decoders and fusion with MFCC and SDC features", In INTERSPEECH-2007, 194-197.