Regularized minimum class variance extreme learning machine for language recognition

Abstract

Support vector machines (SVMs) have played an important role in the state-of-the-art language recognition systems. The recently developed extreme learning machine (ELM) tends to have better scalability and achieve similar or much better generalization performance at much faster learning speed than traditional SVM. Inspired by the excellent feature of ELM, in this paper, we propose a novel method called regularized minimum class variance extreme learning machine (RMCVELM) for language recognition. The RMCVELM aims at minimizing empirical risk, structural risk, and the intra-class variance of the training data in the decision space simultaneously. The proposed method, which is computationally inexpensive compared to SVM, suggests a new classifier for language recognition and is evaluated on the 2009 National Institute of Standards and Technology (NIST) language recognition evaluation (LRE). Experimental results show that the proposed RMCVELM obtains much better performance than SVM. In addition, the RMCVELM can also be applied to the popular i-vector space and get comparable results to the existing scoring methods.