A Comparison of Classifiers in Performing Speaker Accent Recognition Using MFCCs
This work addresses accent recognition for speech processing applications, but it is incremental as it applies existing methods to a new dataset.
The paper tackled speaker accent recognition by comparing classifiers using MFCC features on a dataset of 330 signals, finding that k-nearest neighbors achieved the highest average test accuracy with efficient computation time.
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