COMPARATIVE STUDY OF DIFFERENT CLASSIFIERS BASED SPEAKER RECOGNITION SYSTEM USING MODIFIED MFCC FOR NOISY ENVIRONMENT

摘要

Speaker recognition has made great progress under the laboratory environment, but in real life the performance of speaker recognition system is affected by various factors including environmental noise. This paper studies the performance of speaker recognition system in noisy environment and presents Speaker recognition system using modified Mel-Frequency Cepstral Coefficients (MFCC) technique based on different classifiers likes Euclidean distance, Back-Propagation Neural Network (BPNN), Self Organizing Map (SOM). Modified Mel-Frequency Cepstral Coefficients (MFCC) technique includes Blackman windowing instead of hamming window. This paper presents comparative plots of different classifiers based on modified Mel-Frequency Cepstral Coefficients (MFCC) technique. Speaker recognition system based on SOM Neural Network classifier provides better recognition rate compare to BPNN and Euclidean Distance based systems.