Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
A Tutorial on Support Vector Machines for Pattern Recognition
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The production and recognition of emotions in speech: features and algorithms
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Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Combination of generative models and SVM based classifier for speech emotion recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
GMM-SVM Kernel with a Bhattacharyya-based distance for speaker recognition
IEEE Transactions on Audio, Speech, and Language Processing
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ACM Transactions on Intelligent Systems and Technology (TIST)
Posterior probability support vector Machines for unbalanced data
IEEE Transactions on Neural Networks
Multiclass Posterior Probability Support Vector Machines
IEEE Transactions on Neural Networks
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Dynamic kernel based support vector machines are used for classification of varying length patterns. This paper explores the use of intermediate matching kernel (IMK) as a dynamic kernel for classification of varying length patterns of long duration speech represented as sets of feature vectors. The main issue in construction of IMK is the choice for the set of virtual feature vectors used to select the local feature vectors for matching. The components of class-independent GMM (CIGMM) have been used earlier as a representation for the set of virtual feature vectors. For every component of CIGMM, a local feature vector each from the two sets of local feature vectors that has the highest probability of belonging to that component is selected and a base kernel is computed between the selected local feature vectors. The IMK is computed as the sum of all the base kernels corresponding to different components of CIGMM. The construction of CIGMM-based IMK does not use the class-specific information, as the local feature vectors are selected using the components of CIGMM that is common for all the classes. We propose two novel methods to build a better discriminatory IMK-based SVM classifier by considering a set of virtual feature vectors specific to each class depending on the approaches to multiclass classification using SVMs. In the first method, we propose a class-wise IMK based SVM for every class by using components of GMM built for a class as the set of virtual feature vectors for that class in the one-against-the-rest approach to multiclass pattern classification. In the second method, we propose a pairwise IMK based SVM for every pair of classes by using components of GMM built for a pair of classes as the set of virtual feature vectors for that pair of classes in the one-against-one approach to multiclass classification. We also proposed to use the mixture coefficient weighted and responsibility term weighted base kernels in computation of class-specific IMKs to improve their discrimination ability. This paper also proposes the posterior probability weighted dynamic kernels to improve their classification performance and reduce the number of support vectors. The performance of the SVM-based classifiers using the proposed class-specific IMKs is studied for speech emotion recognition and speaker identification tasks and compared with that of the SVM-based classifiers using the state-of-the-art dynamic kernels.