TY  - JOUR
T1  - Markov Model Based Real Time Speaker Recognition using K-Means, Fast Fourier Transform and Mel Frequency Cepstral Coefficients
AU  - Borandağ, Emin
PY  - 2019
DA  - September
DO  - 10.18466/cbayarfbe.556936
JF  - Celal Bayar University Journal of Science
JO  - CBUJOS
PB  - Manisa Celal Bayar Üniversitesi
WT  - DergiPark
SN  - 1305-130X
SP  - 287
EP  - 292
VL  - 15
IS  - 3
LA  - en
AB  - In this study, which was carried out using a combination of machinelearning and sound processing methods, a speaker recognition system andapplication were developed using real-time Mel Frequency Cepstral Coefficients(MFCC) features and Markov chain model classifier. A sound sample was takenfrom each speaker for the training of the system and these sound samples wereprocessed in Fast Fourier Transform and MFCC feature extraction algorithms. TheMFCC features were clustered using the k-means clustering algorithm. A Markovchain model was created for each speaker by using the outputs obtained afterclustering. By deducting the characteristic features of the voice of thespeaker, the person who was talking in the society and how long and at whichtime intervals they spoke during the conversation was determined in real timewith high accuracy.
KW  - Real time speaker recognition
KW  - Mel-Frequency
KW  - K-Means
KW  - Machine Learning
KW  - Markov Chain
KW  - Fast Fourier Transform
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UR  - https://doi.org/10.18466/cbayarfbe.556936
L1  - https://dergipark.org.tr/tr/download/article-file/810621
ER  -