dc.contributor.author | Clarkson, T. G. | en |
dc.contributor.author | Christodoulou, Chris C. | en |
dc.contributor.author | Guan, Y. | en |
dc.contributor.author | Gorse, D. | en |
dc.contributor.author | Romano-Critchley, D. A. | en |
dc.contributor.author | Taylor, J. G. | en |
dc.creator | Clarkson, T. G. | en |
dc.creator | Christodoulou, Chris C. | en |
dc.creator | Guan, Y. | en |
dc.creator | Gorse, D. | en |
dc.creator | Romano-Critchley, D. A. | en |
dc.creator | Taylor, J. G. | en |
dc.date.accessioned | 2019-11-13T10:39:24Z | |
dc.date.available | 2019-11-13T10:39:24Z | |
dc.date.issued | 2001 | |
dc.identifier.issn | 1094-6977 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/53775 | |
dc.description.abstract | 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. | en |
dc.source | IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0035245077&doi=10.1109%2f5326.923269&partnerID=40&md5=e4804953025c8c0725a0cb0955c3b8b7 | |
dc.subject | Neural networks | en |
dc.subject | Probability | en |
dc.subject | Vectors | en |
dc.subject | Random access storage | en |
dc.subject | Speech recognition | en |
dc.subject | VLSI circuits | en |
dc.subject | Security systems | en |
dc.subject | Multilayer neural networks | en |
dc.subject | Probabilistic RAM | en |
dc.subject | Reinforcement training | en |
dc.subject | Signal compression | en |
dc.subject | Speaker identification | en |
dc.subject | Speech processing | en |
dc.subject | Time encoded signal processing and recognition | en |
dc.title | Speaker identification for security systems using reinforcement-trained pRAM neural network architectures | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1109/5326.923269 | |
dc.description.volume | 31 | |
dc.description.issue | 1 | |
dc.description.startingpage | 65 | |
dc.description.endingpage | 76 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Cited By :19</p> | en |
dc.source.abbreviation | IEEE Trans Syst Man Cybern Pt C Appl Rev | en |
dc.contributor.orcid | Christodoulou, Chris C. [0000-0001-9398-5256] | |
dc.gnosis.orcid | 0000-0001-9398-5256 | |