基于支持向量机的说话人辨认研究

支持向量机是统计学理论的一个重要的学习方法，也是解决模式识别问题的一个有力的工具，本文提出了用支持向量机来解决说话人辨认问题。结合语音信号的特点，解决了大数据量情况下支持向量机的训练问题。支持向量机对两类的分类问题有着突出的优势，本文用两种判决规则将两类问题应用到多类的识别问题。用支持向量机实现了一个与文本无关的说话人辨认系统，实验表明，本方法有良好的效果。

A study on speaker identification using support vector machine

Support vector machine (SVM) is an important learning method of statistical learning theory, and is also a powerful tool for pattern recognition problems. This paper studies the speaker identification problem using support vector machine, and presents a SVM training method on large-scale training data according to the speech signal. Two decision methods are used to apply binary classification problems into multi-classes classification problems for SVM has a good ability on solving binary classification problems. A text-independent speaker identification system based on SVMs was implemented and the results showed good performance.