Spoken Language Identification Using a New Sequence Kernel-based SVM Back-end Classifier

In this paper, we present a new back-end classifier for GMM-LM based language identification systems. Our new proposed system consists of two main parts, mapping matrix and bank of SVMs. These two parts are located in series after GMM-LM system. The mapping matrix, maps the language models´ output vectors to a new space in which the languages are more separable than before. Then each SVM in the SVM bank separates one language from the others. We used a new sequence kernel for each SVM in the bank. We show that our new sequence kernel-based SVMs separate languages more efficiently than common Gaussian mixture and GLDS SVM back-end classifiers. Also our new mapping matrix outperforms common linear discriminant matrix in separating classes from each other. Using these two parts increases the LID accuracy noticeably in comparison with the other LDA-GMM and LDA-GLDS SVM back-end classifiers. Our experiments on 5 languages from OGI-TS multilanguage task, prove our claim.