ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Articulatory feature recognition using dynamic Bayesian networks
Computer Speech and Language
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Fast training of SVM via morphological clustering for color image segmentation
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Automatic detection of auditory salience with optimized linear filters derived from human annotation
Pattern Recognition Letters
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An important aspect of distinctive feature based approaches to automatic speech recognition is the formulation of a framework for robust detection of these features. We discuss the application of the support vector machines (SVM) that arise when the structural risk minimization principle is applied to such feature detection problems. In particular, we describe the problem of detecting stop consonants in continuous speech and discuss an SVM framework for detecting these sounds. In this paper we use both linear and nonlinear SVMs for stop detection and present experimental results to show that they perform better than a cepstral features based hidden Markov model (HMM) system, on the same task.