Automatic language identification using support vector machines and phonetic N-gram

In this paper, we describe two approaches for language identification (LID) using support vector machines (SVM) and phonetic n-gram. One is to use the language model scores of phone sequences to do SVM training. The other is to use the n-gram probabilities of those phones to train SVM models. For the second approach, we propose a new effective normalization method. In the experiments of 30 s test for 5 languages, our new normalization method shows a relative reduction of 15.8% in terms of equal error rate (EER) compared with the traditional one. And it makes the system using the second approach reaches an EER of 2.4%, a relative reduction of about 35.5% in comparison with the first one. Details of implementation and experimental results are presented in this paper.