Text Dependent Speaker Identification And Intruder Detection System
Abstract
This research paper will present an automatic Speaker Identification System using MFCC (Mel-Frequency Cepstral Coefficients) and BPNN (Back Propagation Neural Network). The objective of this work is to classify 20 speakers’ pattern and to identify each registered speaker correctly while testing with new input speech without any false identification. MFCC is used for the extraction of speech features from each speaker and BPNN is used for identification of the test speaker. The developed classifier model is tested with both registered and unregistered speakers and found that it successfully identifies all the registered speakers correctly and reject the intruder speakers. Scaled conjugate gradient training function is used for training the BPNN. A speech database consisting of 20 speakers is created from a group of 10 male and 10 female speakers with the same sentence spoken twice. The classification accuracy rate obtained from the classification is 92.1% and the correct identification rate obtained is 100%. Matlab simulation tool is used in this work
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