Text dependent speaker recognition using MFCC and LBG VQ

Abstract

Speaker Recognition is a process of automatically recognizing who is speaking on the basis of the individual information included in speech waves. Speaker Recognition is one of the most useful biometric recognition techniques in this world where insecurity is a major threat. Many organizations like banks, institutions, industries etc are currently using this technology for providing greater security to their vast databases.Speaker Recognition mainly involves two modules namely feature extraction and feature matching. Feature extraction is the process that extracts a small amount of data from the speaker’s voice signal that can later be used to represent that speaker.Feature matching involves the actual procedure to identify the unknown speaker by comparing the extracted features from his/her voice input with the ones that are already stored in our speech database.In feature extraction we find the Mel Frequency Cepstrum Coefficients, which are based on the known variation of the human ear’s critical bandwidths with frequency and these, are vector quantized using LBG algorithm resulting in the speaker specific codebook.
In feature matching we find the VQ distortion between the input utterance of an unknown speaker and the codebooks stored in our database. Based on this VQ distortion we decide whether to accept/reject the unknown speakers identity.The system I implemented in my work is 80% accurate in recognizing the correct speaker.