Speaker identification for security systems using reinforcement-trained pRAM neural network architectures
Date
2001ISSN
1094-6977Source
IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and ReviewsVolume
31Issue
1Pages
65-76Google Scholar check
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Four probabilistic pRAM neural network architectures are presented to explain have the different pRAM network architectures perform a classification. In addition, it is shown where the difficulties lie in seperating different speakers using the time encoded signal processing and recognition (TESPAR) representations. A performance of approximately 97% correct classifications is obtained which is similar to results obtained elsewhere.