Multilingual Speaker Recognition using Mel-frequency Cepstral Coefficients and Gaussian Mixture Model

Abstract

Introduction: People can recognize a speaker with the help of their voice via mobile or digital devices. Method: To obtain this congenital human being ability, authentication techniques based on speaker biometrics like automated speaker recognition (ASR) have been proposed. An ASR identifies speakers by speech signals analysis and salient feature extraction from their voices. Result: This will become an important part of recent research in the voice biometrics field. This paper proposes multilingual speaker recognition with the help of MFCC as feature extraction and GMM as classification techniques using various available datasets such as TIMIT, librespeech, etc. Conclusion: The results achieved from the given datasets enhance the recognition rate of 70.98% with MFCC.