C. Chandra Sekhar

Extraction of fixed dimension patterns from varying duration segments of consonant-vowel utterances

Classification models based on multilayer perceptron (MLP) or support vector machine (SVM) have been commonly used for complex pattern classification tasks. These models are suitable for classification of fixed dimension patterns. However, durations of consonant-vowel (CV) utterances vary not only for different classes, but also for a particular CV class. It is necessary to develop a method for representing the CV utterances by patterns of fixed dimension. For CV utterances, vowel onset point (VOP) is the instant at which the consonant part ends and the vowel part begins. Important information necessary for classification of CV utterances is present in the region around the VOP. A segment of fixed duration around the VOP can be processed to extract a pattern of fixed dimension to represent a CV utterance. Accurate detection of vowel onset points is important for recognition of CV utterances of speech. In this paper, we propose an approach for detection of VOP, based on dynamic time alignment between a reference pattern of a CV class and the pattern of an utterance of that class. The results of studies show that the hypothesised VOPs using the proposed approach have less deviation from their actual locations.

Spatiostructural Features for Recognition of Online Handwritten Characters in Devanagari and Tamil Scripts

The spatiostructural features proposed for recognition of online handwritten characters refer to offline-like features that convey information about both the positional and structural (shape) characteristics of the handwriting unit. This paper demonstrates the effectiveness of representing an online handwritten stroke using spatiostructural features, as indicated by its effect on the stroke classification accuracy by a Support Vector Machine (SVM) based classifier. The study has been done on two major Indian writing systems, Devanagari and Tamil. The importance of localization information of the structural features and handling of translational variance is studied using appropriate approaches to zoning the handwritten character.

Modular Approach to Recognition of Strokes in Telugu Script

In this paper, we address some issues in developing an online handwritten character recognition(HCR) system for an Indian language script, Telugu. The number of characters in this script is estimated to be around 5000. A character in this script is written as a sequence of strokes. The set of strokes in Telugu consists of 253 unique strokes. As the similarity among several strokes is high, we propose a modular approach for recognition of strokes. Based on the relative position of a stroke in a character, the stroke set has been divided into three subsets, namely, baseline strokes, bottom strokes and top strokes. Classifiers for the different subsets of strokes are built using support vector machines(SVMs). We study the performance of the classifiers for subsets of strokes and propose methods to improve their performance. A comparative study using hidden Markov models(HMMs) shows that the SVM based approach gives a significantly better performance.

A constraint satisfaction model for recognition of stop consonant-vowel (SCV) utterances

We propose a model for recognition of utterances of consonant-vowel (CV) units. The acoustic-phonetic knowledge of the CV classes is incorporated in the form of constraints of a constraint satisfaction model. The model combines evidence from multiple classifiers. The significant feature of this model is that discrimination of the CV units could be enhanced by a combination of even weak evidence derived from the features. The evidence is obtained from multilayer feedforward neural networks trained for subgroups of CV classes. The evidence is enhanced using a set of feedback subnetworks in the constraint satisfaction model. The weights for the connections in the feedback subnetworks are derived using acoustic-phonetic knowledge and the performance statistics of the trained networks. The performance of the proposed model is demonstrated for recognition of utterances of a large number (80) of stop consonant-vowel units for the Indian language Hindi.

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.

Variational Gaussian Mixture Models for Speech Emotion Recognition

In this paper applicability of variational methods for estimation of parameters of models used for speech emotion recognition is discussed.When the amount of data available is not adequate for training complex models, variational Bayesian method helps in training models with less amount of data. It also helps in determining the optimal complexity of the model. Our studies on Berlin emotional speech database show that variational methods perform better than maximum likelihood approach to estimate parameters of Gaussian mixture models used in speech emotion recognition.

Spotting multilingual consonant-vowel units of speech using neural network models

Multilingual speech recognition system is required for tasks that use several languages in one speech recognition application. In this paper, we propose an approach for multilingual speech recognition by spotting consonant-vowel (CV) units. The important features of spotting approach are that there is no need for automatic segmentation of speech and it is not necessary to use models for higher level units to recognise the CV units. The main issues in spotting multilingual CV units are the location of anchor points and labeling the regions around these anchor points using suitable classifiers. The vowel onset points (VOPs) have been used as anchor points. The distribution capturing ability of autoassociative neural network (AANN) models is explored for detection of VOPs in continuous speech. We explore classification models such as support vector machines (SVMs) which are capable of discriminating confusable classes of CV units and generalisation from limited amount of training data. The data for similar CV units across languages are shared to train the classifiers for recognition of CV units of speech in multiple languages. We study the spotting approach for recognition of a large number of CV units in the broadcast news corpus of three Indian languages.

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.

Combination of generative models and SVM based classifier for speech emotion recognition

Modeling time series data of varying length is important in different domains. There are two paradigms for modeling the varying length sequential data. Tasks such as speech recognition need modeling the temporal dynamics and the correlations among the features. Hidden Markov models (HMM) are used for these tasks. In tasks such as speaker recognition, audio classification and speech emotion recognition, modeling the temporal dynamics is not critical. Gaussian mixture models (GMM) are commonly used for these tasks. Generative models such as HMMs and GMMs focus on estimating the density of the data and are not suitable for classifying the data of confusable classes. Discriminative classifiers such as support vector machines (SVM) are suitable for the fixed dimensional patterns. In this paper, we propose a hybrid framework where a generative front end is used for representing the varying length time series data and then a discriminative model is used for classification. A score based approach and a segment modeling based approach are proposed in this framework. Both the approaches are applied for speech emotion recognition. The performance is compared with that of an SVM classifier that uses different statistical features and also with that of the GMM classifiers that use maximum likelihood method and the variational Bayes method for parameter estimation. Both the proposed approaches outperform the methods used for comparison.