  An algorithm 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
