Open AccessVOICE RECOGNITION SECURITY SYSTEM USING MEL-FREQUENCY CEPSTRUM COEFFICIENTS
Author(s) -
P. Mahalakshmi,
M. Muruganandam,
A. Sharmila
Publication year - 2016
Publication title -
asian journal of pharmaceutical and clinical research
Language(s) - English
Resource type - Journals
eISSN - 2455-3891
pISSN - 0974-2441
DOI - 10.22159/ajpcr.2016.v9s3.13633
Subject(s) - mel frequency cepstrum , speech recognition , vector quantization , computer science , speaker recognition , euclidean distance , hidden markov model , feature vector , pattern recognition (psychology) , cepstrum , matlab , artificial intelligence , feature (linguistics) , feature extraction , operating system , linguistics , philosophy
ABSTRACTObjective: Voice Recognition is a fascinating field spanning several areas of computer science and mathematics. Reliable speaker recognition is a hardproblem, requiring a combination of many techniques; however modern methods have been able to achieve an impressive degree of accuracy. Theobjective of this work is to examine various speech and speaker recognition techniques and to apply them to build a simple voice recognition system.Method: The project is implemented on software which uses different techniques such as Mel frequency Cepstrum Coefficient (MFCC), VectorQuantization (VQ) which are implemented using MATLAB.Results: MFCC is used to extract the characteristics from the input speech signal with respect to a particular word uttered by a particular speaker. VQcodebook is generated by clustering the training feature vectors of each speaker and then stored in the speaker database.Conclusion: Verification of the speaker is carried out using Euclidian Distance. For voice recognition we implement the MFCC approach using softwareplatform MatlabR2013b.Keywords: Mel-frequency cepstrum coefficient, Vector quantization, Voice recognition, Hidden Markov model, Euclidean distance.