Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition

Abstract

We develop a framework for large margin classification by Gaussian mixture models (GMMs). Large margin GMMs have many parallels to support vector machines (SVMs) but use ellipsoids to model classes instead of half-spaces. Model parameters are trained discriminatively to maximize the margin of correct classification, as measured in terms of Mahalanobis… (More)
DOI: 10.1109/ICASSP.2006.1660008

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@article{Sha2006LargeMG, title={Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition}, author={Fei Sha and Lawrence K. Saul}, journal={2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings}, year={2006}, volume={1}, pages={I-I} }