# A Comparison of Classifiers in Performing Speaker Accent Recognition Using MFCCs

arXiv: https://arxiv.org/abs/1501.07866 · HTML: https://mlsanity.com/paper/1501.07866/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2015.

## Built on by (most cited first, 1 of 1)

- [On Exact Bayesian Credible Sets for Classification and Pattern Recognition](https://mlsanity.com/paper/2308.11037/) (2023)

## Abstract (arXiv)

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