Aroor Dinesh Dileep

Representation and feature selection using multiple kernel learning

Multiple kernel learning (MKL) approach for selecting and combining different representations of a data is presented. Selection of features from a representation of data using the MKL approach is also addressed. A base kernel function is used for each representation as well as for each feature from a representation. A new kernel is obtained as a linear combination of base kernels, weighted according to the relevance of representation or feature. The MKL approach helps to select and combine the representations as well as to select features from a representation. Issues in the MKL algorithm are addressed in the framework of support vector machines (SVM). Studies on the representation and feature selection are presented for an image categorization task.

Identification of Block Ciphers using Support Vector Machines

In this paper, we propose an approach for identification of encryption method for block ciphers using support vector machines. The task of identification of encryption method from cipher text only is considered as a document categorization task. We address the issues in representing a cipher text by a document vector. We consider the common dictionary based method and the class specific dictionary based method for generating a document vector from a cipher text. As the dimension of document vector is large, support vector machines based classifiers are considered for identification of encryption method. We present the performance of the proposed approach for cipher texts generated using five block ciphers.

GMM-Based Intermediate Matching Kernel for Classification of Varying Length Patterns of Long Duration Speech Using Support Vector Machines

Dynamic kernel (DK)-based support vector machines are used for the classification of varying length patterns. This paper explores the use of intermediate matching kernel (IMK) as a DK 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. This paper proposes to use components of class-independent Gaussian mixture model (CIGMM) 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. It is proposed to use the responsibility term weighted base kernels in computation of IMK to improve its discrimination ability. This paper also proposes the posterior probability weighted DKs (including the proposed IMKs) to improve their classification performance and reduce the number of support vectors. The performance of the support vector machine (SVM)-based classifiers using the proposed 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 DKs.

Class-specific GMM based intermediate matching kernel for classification of varying length patterns of long duration speech using support vector machines

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.

Speaker recognition using pyramid match kernel based support vector machines

Gaussian mixture model (GMM) based approaches have been commonly used for speaker recognition tasks. Methods for estimation of parameters of GMMs include the expectation-maximization method which is a non-discriminative learning based method. Discriminative classifier based approaches to speaker recognition include support vector machine (SVM) based classifiers using dynamic kernels such as generalized linear discriminant sequence kernel, probabilistic sequence kernel, GMM supervector kernel, GMM-UBM mean interval kernel (GUMI) and intermediate matching kernel. Recently, the pyramid match kernel (PMK) using grids in the feature space as histogram bins and vocabulary-guided PMK (VGPMK) using clusters in the feature space as histogram bins have been proposed for recognition of objects in an image represented as a set of local feature vectors. In PMK, a set of feature vectors is mapped onto a multi-resolution histogram pyramid. The kernel is computed between a pair of examples by comparing the pyramids using a weighted histogram intersection function at each level of pyramid. We propose to use the PMK-based SVM classifier for speaker identification and verification from the speech signal of an utterance represented as a set of local feature vectors. The main issue in building the PMK-based SVM classifier is construction of a pyramid of histograms. We first propose to form hard clusters, using k-means clustering method, with increasing number of clusters at different levels of pyramid to design the codebook-based PMK (CBPMK). Then we propose the GMM-based PMK (GMMPMK) that uses soft clustering. We compare the performance of the GMM-based approaches, and the PMK and other dynamic kernel SVM-based approaches to speaker identification and verification. The 2002 and 2003 NIST speaker recognition corpora are used in evaluation of different approaches to speaker identification and verification. Results of our studies show that the dynamic kernel SVM-based approaches give a significantly better performance than the state-of-the-art GMM-based approaches. For speaker recognition task, the GMMPMK-based SVM gives a performance that is better than that of SVMs using many other dynamic kernels and comparable to that of SVMs using state-of-the-art dynamic kernel, GUMI kernel. The storage requirements of the GMMPMK-based SVMs are less than that of SVMs using any other dynamic kernel.

HMM Based Intermediate Matching Kernel for Classification of Sequential Patterns of Speech Using Support Vector Machines

In this paper, we address the issues in the design of an intermediate matching kernel (IMK) for classification of sequential patterns using support vector machine (SVM) based classifier for tasks such as speech recognition. Specifically, we address the issues in constructing a kernel for matching sequences of feature vectors extracted from the speech signal data of utterances. The codebook based IMK and Gaussian mixture model (GMM) based IMK have been proposed earlier for matching the varying length patterns represented as sets of features vectors for tasks such as image classification and speaker recognition. These methods consider the centers of clusters and the components of GMM as the virtual feature vectors used in the design of IMK. As these methods do not use sequence information in matching the patterns, these methods are not suitable for matching sequential patterns. We propose the hidden Markov model (HMM) based IMK for matching sequential patterns of varying length. We consider two approaches to design the HMM-based IMK. In the first approach, each of the two sequences to be matched is segmented into subsequences with each subsequence aligned to a state of the HMM. Then the HMM-based IMK is constructed as a combination of state-specific GMM-based IMKs that match the subsequences aligned with the particular states of the HMM. In the second approach, the HMM-based IMK is constructed without segmenting sequences, and by matching the local feature vectors selected using the responsibility terms that account for being in a state and generating the feature vectors by a component of the GMM of that state. We study the performance of the SVM based classifiers using the proposed HMM-based IMK for recognition of isolated utterances of E-set in English alphabet and recognition of consonent–vowel segments in Hindi language.

Block cipher identification using support vector classification and regression

Abstract In this paper, we propose two approaches for identification of block ciphers using support vector machines. Identification of the encryption method for block ciphers is considered as a pattern classification task. In the first approach, the cipher text is given as input to the classifier. In the second approach, the partially decrypted text derived from a cipher text is given as input to the classifier. Support vector regression based hetero-association model is used to derive the partially decrypted text. The cipher text and partially decrypted text are considered as documents and the task of identification of encryption method is considered as a document categorization task. We address the issues in representing a document by a feature vector. Three methods are considered for representation of a document by a feature vector. In the first method, a document is represented as a vector of integers. In the second method, a document is represented by a block level similarity based feature vector. Subsequence kernels are used to measure the similarity between a pair of blocks. In the third method, a document is represented by a distance based feature vector. We present the performance of the proposed approaches for cipher texts generated using block ciphers.

Bird Call Identification Using Dynamic Kernel Based Support Vector Machines and Deep Neural Networks

In this paper, we apply speech and audio processing techniques to bird vocalizations and for the classification of birds found in the lower Himalayan regions. Mel frequency cepstral coefficients (MFCC) are extracted from each recording. As a result, the recordings are now represented as varying length sets of feature vectors. Dynamic kernel based support vector machines (SVMs) and deep neural networks (DNNs) are popularly used for the classification of such varying length patterns obtained from speech signals. In this work, we propose to use dynamic kernel based SVMs and DNNs for classification of bird calls represented as sets of feature vectors. Results of our studies show that both approaches give comparable performance.

Text classification using combined sparse representation classifiers and support vector machines

Text classification is an important task in managing huge repository of textual content prevailing in various domains. In this paper, we propose to use sparse representation classifier (SRC) and support vector machines (SVMs) based classifiers using frequency-based kernels for text classification. We consider term-frequency (TF) representation for a text document. The sparse representation of an example is obtained by using an overcomplete dictionary made up of TF vectors corresponding to all the training documents [1]. We propose to seed the dictionary using principal components of TF vector representation corresponding to training text documents. SVM-based text classifiers use linear kernel or Gaussian kernel on the TF vector representation of documents. TF representation being a non-negative, histogram representation, we propose to build SVM-based text classifiers using frequency-based kernels such as histogram intersection kernel, Chi-square (X2) kernel and Hellingers kernel. It is observed that the examples misclassified by one classifier is correctly classified in another classifier. To take advantage of the various classifiers, we introduce an approach to combine classifiers to improve the performance of text classification. The effectiveness of all the proposed techniques for text classification is demonstrated on 20 Newsgroup Corpus.

Speaker Identification Using Intermediate Matching Kernel-Based Support Vector Machines

Gaussian mixture model (GMM) based approaches have been commonly used for speaker recognition tasks. Methods for estimation of parameters of GMMs include the expectation-maximization method which is a non-discriminative learning based method and the large margin method which is a discriminative learning based method. Discriminative classifier based approaches to speaker recognition include support vector machine (SVM) based classifiers using dynamic kernels such as generalized linear discriminant sequence kernel, probabilistic sequence kernel, GMM supervector kernel and Bhattacharyya distance based kernel . Recently, the intermediate matching kernel (IMK) has been proposed as a dynamic kernel for recognition of objects in an image represented using a set of local feature vectors. The IMK-based SVMs give a better performance than the state-of-the-art GMM-based approaches for speaker identification tasks, because they are well suited for meeting the basic challenge of providing reliable scores of intra-speaker variation of suspects and scores of inter-speaker variation of the potential population which is crucial to law enforcement and counter terrorism agencies in evaluating the strength of the evidence at hand. Thus, the IMK-based SVMs can be used to build the speaker recognition models in the FSR (forensic speaker recognition) systems. However, it is necessary to develop techniques to determine the strength of evidence from the outputs of SVM-based models. The SVM-based models are trained using discriminative methods and their generalization ability is good. We propose to use the IMK-based SVM classifier for speaker identification from the speech signal of an utterance represented as a set of local feature vectors. The main issue in building the IMK-based SVM classifier is selection of the virtual feature vectors using which the local feature vectors from the representations of two different utterances are matched. We explore the use of components of universal background GMM as the set of virtual feature vectors. We compare the performance of the GMM-based approaches and the dynamic kernel SVM-based approaches to speaker identification. The 2002 and 2003 NIST speaker recognition corpora are used in evaluation of different approaches to speaker identification. Results of our studies show that the dynamic kernel SVM-based approaches give a significantly better performance than the GMM-based approaches. For speaker identification task, the IMK-based SVM gives a performance that is comparable to that of SVMs using any of the other dynamic kernels. The storage requirements and the computational complexity of the IMK-based SVMs are less than of SVMs using any of the other dynamic kernels.