Improved language recognition using better phonetic decoders and fusion with MFCC and SDC features
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
International Speech Communication AssociationDate
2007-08Citation
8th Annual Conference of the International Speech Communication Association. August 27-31, 2007ISSN
1990-9772Funded by
This work was funded by the Spanish Ministry of Science and Technology under project TEC2006-13170-C02-01.Editor's Version
http://www.isca-speech.org/archive/interspeech_2007/i07_0194.htmlSubjects
Language recognition; PPRLM; SVM; InformáticaNote
Proceedings of Interspeech 2007, Antwerp (Belgium)Rights
© 2007 ISCAAbstract
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.
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Google Scholar:Toledano, Doroteo T.
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González Domínguez, Javier
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Abejón González, Alejandro
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Spada, Danilo
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Mateos García, Ismael
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González Rodríguez, Joaquín
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