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Pedersen, C. and Diedrich, J. (2008) Accent in Speech Samples: Support Vector Machines for Classification and Rule Extraction. In: Kacprzyk, J., Ed., Studies in Computational Intelligence, Springer-Verlag, Berlin, Vol. 80, 205-226.

has been cited by the following article:

• TITLE: A Comparison of Classifiers in Performing Speaker Accent Recognition Using MFCCs

  AUTHORS: Zichen Ma, Ernest Fokoué

  KEYWORDS: Speaker Accent Recognition, Mel-Frequency Cepstral Coefficients (MFCCs), Discriminant Analysis, Support Vector Machines (SVMs), k-Nearest Neighbors

  JOURNAL NAME: Open Journal of Statistics, Vol.4 No.4, June 20, 2014

  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.