Ensemble Feature Selection for Automatic Speech Recognition
David Gelbart and Nelson Morgan
Automatic speech recognition performance often benefits from a multi-stream approach where multiple classifiers that each use a different feature extraction method are run in parallel and have their decisions combined. However, how best to distribute the possible features among the classifiers is an open problem. Usually, features coming from a particular feature extraction method are treated as a single, indivisible group, of which either all or no members are assigned to a classifier in the system. We are working on a different approach in which an automatic search procedure is used to assign features to classifiers at the level of individual features, using an objective function related to the classification accuracy of the resulting multi-stream system. We hope this approach can increase ASR accuracy and give insight into the relative usefulness of different features.