Speech Communication - Special issue on speech processing in adverse conditions
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Support Vector Machines for Text Categorization
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 4 - Volume 4
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
AdaBoost with SVM-based component classifiers
Engineering Applications of Artificial Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast approach for overcomplete sparse decomposition based on smoothed l0 norm
IEEE Transactions on Signal Processing
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
An efficient speech recognition system in adverse conditions using the nonparametric regression
Engineering Applications of Artificial Intelligence
Improved support vector clustering
Engineering Applications of Artificial Intelligence
A wavelet-based image denoising using least squares support vector machine
Engineering Applications of Artificial Intelligence
An online algorithm for hierarchical phoneme classification
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
A Large Margin Algorithm for Speech-to-Phoneme and Music-to-Score Alignment
IEEE Transactions on Audio, Speech, and Language Processing
Exemplar-Based Sparse Representation Features: From TIMIT to LVCSR
IEEE Transactions on Audio, Speech, and Language Processing
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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.