| SVM with discriminative dynamic time alignment |
| |
| 作者姓名： | 王欢良 韩纪庆 李海峰 |
| |
| 作者单位： | School of Computer Science and Technology Harbin Institute of Technology，School of Computer Science and Technology Harbin Institute of Technology，School of Computer Science and Technology Harbin Institute of Technology，Harbin 150001 China，Harbin 150001 China，Harbin 150001 China |
| |
| 基金项目： | Sponsored by the National Natural Science Foundation of China(Grant No. 60575030)，the Scientific Research Foundation of Harbin Institute of Technol-ogy (Grant No. HIT.2002.70)，the Heilongjiang Scientific Research Foundation for Scholars Returned from Abroad(Grant No.LC03C10) |
| |
| 摘 要： | In the past several years, support vector machines (SVM) have achieved a huge success in many fields, especially in pattern recognition. But the standard SVM cannot deal with length-variable vectors, which is one severe obstacle for its applications to some important areas, such as speech recognition and part-of-speech tagging. The paper proposed a novel SVM with discriminative dynamic time alignment (DDTA-SVM) to solve this problem. When training DDTA-SVM classifier, according to the category information of the training samples, different time alignment strategies were adopted to manipulate them in the kernel functions, which contributed to great improvement for training speed and generalization capability of the classifier. Since the alignment operator was embedded in kernel functions, the training algorithms of standard SVM were still compatible in DDTA-SVM. In order to increase the reliability of the classification, a new classification algorithm was suggested. The preliminary experimental results on Chinese confusable syllables speech classification task show that DDTA-SVM obtains faster convergence speed and better classification performance than dynamic time alignment kernel SVM (DTAK-SVM). Moreover, DDTA-SVM also gives higher classification precision compared to the conventional HMM. This proves that the proposed method is effective, especially for confusable length-variable pattern classification tasks.
|
| 关 键 词： | 支持向量机 计算机技术 计算方法 编程语言 |
| 文章编号： | 1005-9113（2007）05-0598-06 |
| 修稿时间： | 2005-06-10 |
SVM with discriminative dynamic time alignment |
| |
| WANG Huan-liang,HAN Ji-qing,LI Hai-feng.SVM with discriminative dynamic time alignment[J].Journal of Harbin Institute of Technology,2007,14(5):598-603. |
| |
| Authors: | WANG Huan-liang HAN Ji-qing LI Hai-feng |
| |
| Affiliation: | School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China |
| |
| Abstract: | In the past several years, support vector machines (SVM) have achieved a huge success in many fields, especially in pattern recognition. But the standard SVM cannot deal with length-variable vectors, which is one severe obstacle for its applications to some important areas, such as speech recognition and part-of-speech tagging. The paper proposed a novel SVM with discriminative dynamic time alignment (DDTA-SVM) to solve this problem. When training DDTA-SVM classifier, according to the category information of the training samples, different time alignment strategies were adopted to manipulate them in the kernel functions, which contributed to great improvement for training speed and generalization capability of the classifier. Since the alignment operator was embedded in kernel functions, the training algorithms of standard SVM were still compatible in DDTA-SVM. In order to increase the reliability of the classification, a new classification algorithm was suggested. The preliminary experimental results on Chinese confusable syllables speech classification task show that DDTA-SVM obtains faster convergence speed and better classification performance than dynamic time alignment kernel SVM (DTAK-SVM). Moreover, DDTA-SVM also gives higher classification precision compared to the conventional HMM. This proves that the proposed method is effective, especially for confusable length-variable pattern classification tasks. |
| |
| Keywords: | support vector machines dynamic time alignment kernel function speech recognition |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
