Optimized Discriminative Kernel for SVM Scoring and Its Application to Speaker Verification

The decision-making process of many binary classification systems is based on the likelihood ratio (LR) scores of test patterns. This paper shows that LR scores can be expressed in terms of the similarity between the supervectors (SVs) formed by stacking the mean vectors of Gaussian mixture models c...

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Vydané v:	IEEE transactions on neural networks Ročník 22; číslo 2; s. 173 - 185
Hlavní autori:	Zhang, Shi-Xiong, Mak, Man-Wai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.02.2011 Institute of Electrical and Electronics Engineers
Predmet:	Acoustic signal processing Acoustics Adaptation model Algorithms Applied sciences Artificial Intelligence Classification - methods Computational modeling Computer science; control theory; systems Data processing. List processing. Character string processing Discrimination Learning Exact sciences and technology Fundamental areas of phenomenology (including applications) Hilbert space Humans Kernel Kernel optimization Kernels Linear Models Mathematical models Measurement Memory organisation. Data processing Neural networks Neural Networks (Computer) Normal Distribution Optimization Pattern Recognition, Automated - methods Physics Scoring sequence kernels Similarity Software speaker verification Speech Speech Recognition Software - standards Stacking Support vector machines Voice - physiology Biometrics Density estimation Automatic classification Score test Similarity Binary system Training analysis Acoustics Modeling Discriminant function Optimization Kernel optimization Vector support machine Hilbert space Speaker verification Binary classification Mixed distribution Statistical analysis sequence kernels support vector machines Decision support system Decision making Likelihood ratio Regression analysis Speaker recognition Similarity solution Experimental study Kernel method Kernel function Gaussian process Speech recognition
ISSN:	1045-9227, 1941-0093, 1941-0093
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