Title :
HIERtalker: a default hierarchy of high order neural networks that learns to read English aloud
Author :
An, Z.G. ; Mniszewski, S.M. ; Lee, Y.C. ; Papcun, G. ; Doolen, G.D.
Author_Institution :
Los Alamos Nat. Lab., NM, USA
Abstract :
A learning algorithm based on a default hierarchy of high-order neural networks has been developed that is able to generalize as well as handle exceptions. It learns the ´building blocks´ or clusters of symbols in a stream that appear repeatedly and that convey certain messages. The default hierarchy prevents a combinatoric generation of rules. A simulator of such hierarchy, HIERtalker, has been applied to the conversion of English words to phonemes. Accuracy is 99% for trained words and ranges from 76% to 96% for sets of new words.<>
Keywords :
hierarchical systems; natural languages; neural nets; pattern recognition; speech synthesis; English; HIERtalker; building blocks; default hierarchy; high order neural networks; pattern recognition; phonemes; reading aloud; symbol cluster analysis; Hierarchical systems; Natural languages; Neural networks; Pattern recognition; Speech synthesis;
Conference_Titel :
Neural Networks, 1988., IEEE International Conference on
Conference_Location :
San Diego, CA, USA
DOI :
10.1109/ICNN.1988.23932