Submodular feature selection for high-dimensional acoustic score spaces

We apply methods for selecting subsets of dimensions from high-dimensional score spaces, and subsets of data for training, using submodular function optimization. Submodular functions provide theoretical performance guarantees while simultaneously retaining extremely fast and scalable optimization via an accelerated greedy algorithm. We evaluate this approach on two applications: data subset selection for phone recognizer training, and semi-supervised learning for phone segment classification. Interestingly, the first application uses submodularity twice: first for score space sub-selection and then for data subset selection. Our approach is computationally efficient but still consistently outperforms a number of baseline methods.