Distinctive feature detection using support vector machines


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.

DOI: 10.1109/ICASSP.1999.758153

Extracted Key Phrases

4 Figures and Tables


Citations per Year

74 Citations

Semantic Scholar estimates that this publication has 74 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Niyogi1999DistinctiveFD, title={Distinctive feature detection using support vector machines}, author={Partha Niyogi and Christopher J. C. Burges and Padma Ramesh}, booktitle={ICASSP}, year={1999} }