The current state-of-the-art for acoustic language recognition is an i-vector classifier followed by a discriminatively-trained multiclass back-end. This paper presents a unified approach, where a Gaussian i-vector classifier is trained using Maximum Mutual Information (MMI) to directly optimize the multiclass calibration criterion, so that no separate back-end is needed. The system is extended...

Ensemble methods are able to improve the predictive performance of many base classifiers. In this paper, we consider two ensemble learning techniques, bagging and random forests, and apply them to Binary SVM Decision Tree (SVM-BDT). Binary SVM Decision Tree is a tree based architecture that utilizes support vector machines for solving multiclass problems. It takes advantage of both the efficien...

Named Entity Recognition (NER) seeks to locate and classify atomic elements in text into predefined categories such as names of person, organization, location, Quantities, Percentage etc. Named entities tell us the roles of each meaning bearing word in a sentence and hence identification of these entities certainly helps us to extract the essence of the text which is very important in Question ...

This thesis examines the use of support vector machines for active learning using linear, polynomial and radial basis function kernels. In our experiments we used named entity recognition which was treated as a binary task and as a multiclass task and we also tackled shallow parsing. We report savings in annotation costs ranging from 80% to 95% depending on the task. We observed that the distri...

Tone and intonation play a crucial role across many languages. However, the use and structure of tone varies widely, ranging from lexical tone which determines word identity to pitch accent signalling information status. In this paper, we employ a uniform representation of acoustic features for recognition of both Mandarin tone and English pitch accent. The representation captures both local to...

This paper presents a study on the use of Support Vector Machines (SVMs) in classifying Malayalam Consonant – Vowel (CV) speech unit by comparing it to two other classification algorithms namely Artificial Neural Network (ANN) and k – Nearest Neighbourhood (k – NN). We extend SVM to combine many two class classifiers into multiclass classifier using Decision Directed Acyclic Graph (DDAG) algori...

As a very effective method for universal purpose pattern recognition, support vector machine (SVM) was proposed for dichotomic classification problem, which exhibits a remarkable resistance to overfitting, a feature explained by the fact that it directly implements the principle of structural risk minimization. However, in real world, most of classification problems consist of multiple categori...