Regularized minimum class variance extreme learning machine for language recognition
详细信息 查看全文
- 作者：Jiaming Xu ; Wei-Qiang Zhang ; Jia Liu…
- 关键词：Language recognition ; Extreme learning machine ; Single ; hidden layer feedforward neural networks ; Support vector machine
- 刊名：EURASIP Journal on Audio, Speech, and Music Processing
- 出版年：2015
- 出版时间：December 2015
- 年：2015
- 卷：2015
- 期：1
- 全文大小：1,264 KB
- 刊物主题：Signal, Image and Speech Processing;
- 出版者：Springer International Publishing
- ISSN：1687-4722
文摘
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.