Markov Model Based Real Time Speaker Recognition using K-Means, Fast Fourier Transform and Mel Frequency Cepstral Coefficients

Year 2019, , 287 - 292, 30.09.2019

Emin Borandağ

https://doi.org/10.18466/cbayarfbe.556936

Abstract

In this study, which was carried out using a combination of machine
learning and sound processing methods, a speaker recognition system and
application were developed using real-time Mel Frequency Cepstral Coefficients
(MFCC) features and Markov chain model classifier. A sound sample was taken
from each speaker for the training of the system and these sound samples were
processed in Fast Fourier Transform and MFCC feature extraction algorithms. The
MFCC features were clustered using the k-means clustering algorithm. A Markov
chain model was created for each speaker by using the outputs obtained after
clustering. By deducting the characteristic features of the voice of the
speaker, the person who was talking in the society and how long and at which
time intervals they spoke during the conversation was determined in real time
with high accuracy.

Keywords

Real time speaker recognition, Mel-Frequency, K-Means, Machine Learning, Markov Chain, Fast Fourier Transform

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