Study to Multi-Twin Support Vector Machines and Its Applications in Speaker Recognition

This paper proposed a new algorithm named multi-twin support vector machines (MTSVM). At the same time, its application in speaker recognition was studied. The MTSVM tried to find nonparallel plane for every class which the data in the same class are closer to, and the data in the other classes are as far as possible. The MTSVM is different from the normal one-to-all multi-class twin support vector machines (TSVM) where the constrains from other classes are distributed in one quadratic programming problem (QPP). However, in MTSVM, the constraint from every other class is acted on the QPP separately. The feasibility and validity of MTSVM in artificial data and Chains Corpus for speaker recognition are showed in a series of experiments.