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A Comparison of Classifiers in Performing Speaker Accent Recognition Using MFCCs
Center for Quality and Applied Statistics, Rochester Institute of Technology, Rochester, USA
Center for Quality and Applied Statistics, Rochester Institute of Technology, Rochester, USA
- 1 Center for Quality and Applied Statistics, Rochester Institute of Technology, Rochester, USA
- 2 Center for Quality and Applied Statistics, Rochester Institute of Technology, Rochester, USA
Open Journal of Statistics·Volume 04 (2014)·Pages 258–266·Published 20 June 2014·DOI10.4236/ojs.2014.44025
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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.
KeywordsSpeaker Accent RecognitionMel-Frequency Cepstral Coefficients (MFCCs)Discriminant AnalysisSupport Vector Machines (SVMs)k-Nearest Neighbors
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