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.
Cite this paper
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