Support vector machines using GMM supervectors for speaker verification
Support vector machines using GMM supervectors for speaker verification
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DOI：
10.1109/lsp.2006.870086
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发表时间：
2006-05-01
影响因子：
3.9
通讯作者：
Reynolds, DA
中科院分区：
文献类型：
--
作者：
Campbell, WM;Sturim, DE;Reynolds, DA
高斯混合模型（GSMs）已经被证明是非常成功的文本无关的说话人识别。GMM模型的标准训练方法是基于来自目标说话人的语音使用混合分量的均值的MAP自适应。最近的方法在补偿扬声器和通道的变化提出了堆叠的GMM模型的手段，以形成一个GMM平均超向量的想法。我们研究的想法，使用GMM超向量的支持向量机（SVM）分类器。我们提出了两个新的SVM核GMM模型之间的距离度量的基础上。我们表明，这些SVM内核产生良好的分类精度在NIST的说话人识别评估任务。
Gaussian mixture models (GMMs) have proven extremely successful for text-independent speaker recognition. The standard training method for GMM models is to use MAP adaptation of the means of the mixture components based on speech from a target speaker. Recent methods in compensation for speaker and channel variability have proposed the idea of stacking the means of the GMM model to form a GMM mean supervector. We examine the idea of using the GMM supervector in a support vector machine (SVM) classifier. We propose two new SVM kernels based on distance metrics between GMM models. We show that these SVM kernels produce excellent classification accuracy in a NIST speaker recognition evaluation task.