An improved speaker recognition by using VQ & HMM

The domain area of this topic is biometric. Speaker Recognition is biometric system. This paper deals with speaker recognition by HMM (Hidden Markov Model) method. The system is able to recognize the speaker by translating the speech waveform into a set of feature vectors using MelFrequency Cepstral Coefficients (MFCC) technique. But, input speech signals at different time may contain variations. Same speaker may utter the same word at different speed which gives us variation in the total number of MFCC coefficients. Vector Quantization (VQ) is used to make the same number of MFCC coefficients. Hidden Markov Model (HMM) provides a highly reliable way of recognizing a speaker. Hidden Markov Models have been widely used, which are usually considered as a set of states with Markovian properties and observations generated independently of those states. With the help of Viterbi decoding most likely state sequence is obtained. This state sequence is used for speaker recognition. For a database of size 50 in normal environment, obtained result is 98% which is better than previous methods used for speaker recognition.