A Convex-Hull Approach to Sparse Representations for Exemplar-Based Speech Recognition

In this paper, we propose a novel exemplar based technique for classification problems where for every new test sample the classification model is re-estimated from a subset of relevant samples of the training data. We formulate the exemplar-based classification paradigm as a sparse representation (SR) problem, and explore the use of convex hull constraints to enforce both regularization and sparsity. Finally, we utilize the Extended Baum-Welch (EBW) optimization technique to solve the SR problem. We explore our proposed methodology on the TIMIT phonetic classification task, showing that our proposed method offers statistically significant improvement over common classification methods, and provides an accuracy of 82.9%, the best number reported to date.

By: Tara N. Sainath, David Nahamoo, Dimitri Kanevsky, Bhuvana Ramabhadran, Parikshit Shah

Published in: RC25152 in 2011


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