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HIERtalker: a default hierarchy of high order neural networks that learns to read English aloud

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5 Author(s)
An, Z.G. ; Los Alamos Nat. Lab., NM, USA ; Mniszewski, S.M. ; Lee, Y.C. ; Papcun, G.
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Summary form only given. The authors proposed and tested a learning procedure, based on a default hierarchy of high-order neural networks, which exhibited an enhanced capability of generalization and a good efficiency. This architecture is suitable for learning regularities embedded in a stream of information with inherent long range correlations. When applied to the conversion of English works to phonemes, a simulator of such a hierarchy, HIERtalker, achieved an accuracy of typically 99% for the words in the training set, and 96% for new words. Also, HIERtalker used considerably less computer time than NETtalk did. The Hebbian learning rule without any error corrections was also used

Published in:

Artificial Intelligence Applications, 1988., Proceedings of the Fourth Conference on

Date of Conference:

14-18 Mar 1988