Self organizing map and associative memory model hybrid classifier for speaker recognition

Abstract

In this study, self organizing map (SOM) and associative memory model (AMM) artificial neural networks (ANN) are used as hybrid classifier for several speaker recognition experiments. These include text dependent closed-set speaker identification and speaker verification of Turkish speaker set and text independent closed-set speaker identification of a subset of the TIMIT database. Turkish speaker set constitutes 10 speakers with their name and surname. Each utterance is repeated 8 times, 5 of them are used in training and. remaining in the test stages. The subset of the TIMIT database consists 38 speakers from New England region. Each speaker's 10 different utterances are equally selected for using in training and test session. Mel frequency cepstral coefficients (MFCC) method is used for feature extraction of the training and test vectors. When the study is compared with different studies for the same databases, this study gives good results as much as the others.