A Comparison of Classifiers in Performing Speaker Accent Recognition Using MFCCs
Sound
2015-02-02 v1 Applications
Abstract
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
Cite
@article{arxiv.1501.07866,
title = {A Comparison of Classifiers in Performing Speaker Accent Recognition Using MFCCs},
author = {Zichen Ma and Ernest Fokoue},
journal= {arXiv preprint arXiv:1501.07866},
year = {2015}
}
Comments
9 pages, 7 figures