Spoken Language recognition using support vector machines with generative front-end

This paper introduces a spoken language recognition system with a generative front-end and a discriminative backend. The generative front-end is built upon an ensemble of Gaussian densities. These Gaussian densities are trained to represent elementary speech sound units characterizing a wide variety of languages. We formulate the generative front-end in a form of sequence kernel. This sequence kernel transforms a spoken utterance into a feature vector with its attributes representing the occurrence statistics of the speech sound units. A discriminative support vector machine (SVM) then operates on the feature vectors to make classification decision. The proposed language recognition system demonstrates competitive performance on NIST 1996, 2003 and 2005 LRE corpora.