Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers
Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers
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发表时间：
2003-08
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通讯作者：
A. Klautau;N. Jevtic;A. Orlitsky
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作者：
A. Klautau;N. Jevtic;A. Orlitsky
我们表明，基于高斯混合模型（GMM）的分类器可以进行区分训练，以提高准确性。我们描述了一个训练过程的基础上扩展的Baum-Welch算法用于语音识别。我们还比较了新的判别GMM分类器的精度和稀疏度的生成GMM分类器，和核分类器，如支持向量机（SVM）和相关向量机（RVM）。
We show that a classifier based on Gaussian mixture models (GMM) can be trained discriminatively to improve accuracy. We describe a training procedure based on the extended Baum-Welch algorithm used in speech recognition. We also compare the accuracy and degree of sparsity of the new discriminative GMM classifier with those of generative GMM classifiers, and of kernel classifiers, such as support vector machines (SVM) and relevance vector machines (RVM).