基于全变量因子分析和概率线性区分性分析的算法是目前与文本无关的说话人确认的主流算法。该文将全变量分析和支持向量机结合起来，把低维的全变量因子作为支持向量机的输入特征，并采用余弦核函数来增强低维特征的区分性，该系统取得了与当前主流算法相当的性能；进一步，将此系统得分和概率线性鉴别分析系统得分融合起来可以取得明显的性能提升。在NIST 2012说话人评测通用测试条件的女声部分，融合后的系统在情境一和三的检测代价函数相对最好的单系统分别下降了25.1%和25.2%。
Abstract
The total variability factor extractor and the probability linear discriminant analysis (PLDA) algorithms have been the state-of-the-art for text-independent speaker verification. This study combines a support vector machine (SVM) with the PLDA. The low dimensional i-vectors of the total variability system are used as the inputs to the support vector machine, with the cosine kernel function used to achieve better discrimination. This method achieves considerable performance improvement with the PLDA system. Furthermore, the score fusion of the SVM with the PLDA give even better results. Tests were conducted on the female part of the interview section of the NIST 2012 core test corpus. The detection cost function (DCF) of the fusion system was reduced by 25.1% for common condition 1 and 25.2% for condition 3 compared with the best results for a single system.
关键词
说话人确认 /
全变量系统 /
支持向量机 /
核函数
Key words
speaker verification /
total variability /
support vector machine /
kernel function
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参考文献
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