基于线性对数似然核函数的说话人识别

摘要

To improve the performance of a text-independent speaker recognition system, the authors proposed a speaker recognition system based on linear log-likelihood kernel function.The linear log-likelihood kernel compressed the input cepstrum feature sequence of a speaker model by a Gaussian mixture model.The log-likelihood between two utterances was simplified to the distance between the parameters of Gaussian mixture model.Polarization identity was applied to obtain the mapping from a eepstrum feature sequence to a high dimension vector.Support Vector Machine (SVM) was used to train speaker models.The experimental results on National Institute of Standard and Technology show that the proposed kernel has excellent performance.%为了提高文本无关的说话人识别系统的性能,提出了基于线性对数似然核函数的说话人识别系统.线性对数似然核函数利用高斯混合模型对频谱特征序列进行压缩;将频谱特征序列之间的相似程度转化为高斯混合模型参数之间的距离;根据距离表达式,利用极化恒等式求得频谱特征序列向高维矢量空间的映射方法;最后,在高维矢量空间,采用支持向量机(SVM)为目标说话人建立模型.在美国国家标准技术署公布的说话人识别数据库上的实验结果表明,所提核函数具有优异的识别性能.