Paper Details

Speaker verification system identifies the concern person who is speaking, through the specialcharacteristics of voice. Speaker verification is one to one process which is used for various safety measures purposes.Speech/voice has some specific features (e.g., speaking style, voice pitch) which differ person to person. Throughoutverification process large speech data of concern person is not actually required. The acoustic characteristicsinformation is hidden in small data portion. Feature Extraction method can be applied to filter out the specificcharacteristics of large data and can be store in a database after modeling. An EM Algorithm (expectationmaximization) will be used to train the data for the different uttering sounds of voice, hence that database can becomemore efficient. Within the EM algorithm takes multiple iteration to calculate log likelihood value. Especially first valueof Mean is set to some random value. Setting Mean value using Fuzzy C-Mean Clustering reduces number of iterationand increases the accuracy of result of speaker verification.After verification of input speech is to be performed withthe database, after that again Feature Extraction is done using MFCC (Mel-Frequency Cepstral Coefficients) andmodeling will be performed using GMM (Gaussian Mixture Models) on input query signal and matching will beperformed

Parvati J.Chaudhary, Kinjal M.Vagadia, “Principal Component Analysis Based Speaker Verification”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 2, Issue 5, pp. 177-186, May 2015.