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

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.