Speaker Recognition using MFCC and GMM
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I've run the system using the following for training: Speech data(NTIMIT) --> MFCC (feature extraction) --> GMM (modeling)
Speech data(NTIMIT)--> MFCC (feature extraction) --> EM (scores)
the accuracy I am getting is 44% for 461 speakers. it was confirmed by 2 at least(1. Reynolds. 2. Patra) that running such system should give an accuracy of 60.8% for 630 speakers i have done lots of changes in terms of sampling frequency (mainly 8000 or 16000), number of MFCC cepstums, number of MFCC mixtures and iterations and the window size and that was the best percentage I could get.
I am using an MFCC and GMM codes which gave good result with TIMIT
advice would be really appreciated
mamdouh on 4 May 2011
i can tell that you should give more attention to the training data and the prepossessing step .... you can use PCA algorithm for dimensionality reduction and the class separability measure i think it may help
and if you can help me with code of the mfcc and the gmm i'll be thankful Regards.