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 processingmethods, a speaker recognition system and application were developed using real-time Mel FrequencyCepstral 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 were processed in Fast FourierTransform and MFCC feature extraction algorithms. The MFCC features were clustered using the kmeans clustering algorithm. A Markov chain model was created for each speaker by using the outputsobtained after clustering. By deducting the characteristic features of the voice of the speaker, the personwho was talking in the society and how long and at which time intervals they spoke during theconversation was determined in real time with high accuracy.