Language recognition system using language branch discriminative information

Xianliang Wang, Yulong Wan, Lin Yang, Ruohua Zhou, Yonghong Yan

Abstract: This paper presents our study of using language branch discriminative information effectively for language recognition. Language branch variability (LBV) method based on factor analysis techniques is proposed. In LBV method, language branch variability factor is obtained by concatenating low-dimensional factors in the language branch variability spaces. Language models are trained within language branches and between languages. Experiments on NIST 2011 Language Recognition Evaluation (LRE) 30s, 10s and 03s tasks show the proposed LBV method provides stable improvement compared to the state-of-art total variability (TV) approach. In 30-second task, it gains relative improvement by 14.6% in equal error rate (EER) and 12.9% in minimum decision cost value (minDCF), and in new metrics of NIST 2011 LRE, it leads to relative improvement of 7.2%-17.7%.