SR-NBS: A fast sparse representation based N-best class selector for robust phoneme classification

Although exemplar based approaches have shown good accuracy in classification problems, some limitations are observed in the accuracy of exemplar based automatic speech recognition (ASR) applications. The main limitation of these algorithms is their high computational complexity which makes them difficult to extend to ASR applications. In this paper, an N-best class selector is introduced based on sparse representation (SR) and a tree search strategy. In this approach, the classification is fulfilled in three steps. At first, the set of similar training samples for the specific test sample is selected by k-dimensional (KD) tree search algorithm. Then, an SR based N-best class selector is used to limit the classification among certain classes. This makes the classifier adapt to each test sample and reduces the empirical risk. Finally, a well known low error rate classifier is trained by the selected exemplar samples and the trained classifier is employed to classify among the candidate classes. The algorithm is applied to phoneme classification and it is compared with some well-known phoneme classifiers according to accuracy and complexity issues. By this approach, we obtain competitive classification rate with promising computational complexity in comparison with the state of the art phoneme classifiers in clean and well known acoustic noisy environments which causes this approach become a suitable candidate for ASR applications.