中文摘要：

提出了一种用于文本相关说说话人确认技术的i-向量提取方法和L-向量表示.一段用于注册或识别的语音可以用i-向量和L-向量联合表示.同时提出了一种改进的用于支持向量机（SVM）后端分类的核函数,改进的核函数可以同时区分说话人身份的差异和文本内容的差异.在RSR 2015语料集合1和集合2上验证系统的性能,实验结果显示改进的算法相对于传统的i-向量系统的基线能提高至多30%的识别率.

英文摘要：

A text-dependent i-vector extraction scheme and a lexicon-based binary vector（ L-vector）representation are proposed to improve the performance of text-dependent speaker verification. An utterance used for enrollment or test is represented by these two vectors. An improved cosine distance kernel combining i-vector and L-vector is constructed to discriminate both speaker identity and lexical（ or text） diversity with back-end support vector machine（ SVM）. Experiments are conducted on RSR 2015 Corpus part 1 and part 2. The results indicate that at most 30% improvement can be obtained compared with traditional i-vector baseline.