Keyword Spotting Using Support Vector Machines
TSD '02 Proceedings of the 5th International Conference on Text, Speech and Dialogue
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
A hybrid SVM/DDBHMM decision fusion modeling for robust continuous digital speech recognition
Pattern Recognition Letters
Robust ASR using Support Vector Machines
Speech Communication
Activated automotive control of continuous voice using sequential feature segmentation
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
Continuous Malayalam speech recognition using Hidden Markov Models
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
A speech recognizer based on multiclass SVMs with HMM-Guided segmentation
NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing
Efficient binary tree multiclass SVM using genetic algorithms for vowels recognition
CIMMACS'11/ISP'11 Proceedings of the 10th WSEAS international conference on Computational Intelligence, Man-Machine Systems and Cybernetics, and proceedings of the 10th WSEAS international conference on Information Security and Privacy
Evaluation of a set of new ORF kernel functions of SVM for speech recognition
Engineering Applications of Artificial Intelligence
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Hidden Markov models (HMM) with Gaussian mixture observation densities are the dominant approach in speech recognition. These systems typically use a representational model for acoustic modeling which can often be prone to overfitting and does not translate to improved discrimination. We propose a new paradigm centered on principles of structural risk minimization using a discriminative framework for speech recognition based on support vector machines (SVMs). SVMs have the ability to simultaneously optimize the representational and discriminative ability of the acoustic classifiers. We have developed the first SVM-based large vocabulary speech recognition system that improves performance over traditional HMM-based systems. This hybrid system achieves a state-of-the-art word error rate of 10.6% on a continuous alphadigit task—a 10% improvement relative to an HMM system. On SWITCHBOARD, a large vocabulary task, the system improves performance over a traditional HMM system from 41.6% word error rate to 40.6%. This dissertation discusses several practical issues that arise when SVMs are incorporated into the hybrid system.