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involving Mel-Frequency Cepstral Coefficients (MFCCs) is provided to perform
signal feature extraction for the task of speaker accent recognition. Then
different classifiers are compared based on the MFCC feature. For each
signal, the mean vector of MFCC matrix is used as an input vector for pattern
recognition. A sample of 330 signals, containing 165 US voice and 165 non-US
voice, is analyzed. By comparison, k-nearest
neighbors yield the highest average test accuracy, after using a
cross-validation of size 500, and least time being used in the computation.
We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning. Our proposed method handles both high-dimension low-sample size and traditional low-dimensional high-sample size datasets. Essentially, we avoid the computational bottleneck of techniques like Minimum Covariance Determinant (MCD) by computing the needed determinants and associated measures in much lower dimensional subspaces. Both theoretical and computational development of our approach reveal that it is computationally more efficient than the regularized methods in high-dimensional low-sample size, and often competes favorably with existing methods as far as the percentage of correct outlier detection are concerned.