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- W1499519796 abstract "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. >" @default.
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- W1499519796 date "2003-01-06" @default.
- W1499519796 modified "2023-09-26" @default.
- W1499519796 title "HIERtalker: a default hierarchy of high order neural networks that learns to read English aloud" @default.
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- W1499519796 doi "https://doi.org/10.1109/caia.1988.196136" @default.
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