C++ Neural Networks and Fuzzy Logic: Preface

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NETTalk. In 1987, Sejnowski and Rosenberg developed a network connected to a speech

synthesizer that was able to utter English words, being trained to produce phonemes from English

text. The architecture consisted of an input layer window of seven characters. The characters were

part of English text that was scrolled by. The network was trained to pronounce the letter at the center

of the window. The middle layer had 80 neurons, while the output layer consisted of 26 neurons. With

1024 training patterns and 10 cycles, the network started making intelligible speech, similar to the

process of a child learning to talk. After 50 cycles, the network was about 95% accurate. You could

purposely damage the network with the removal of neurons, but this did not cause performance to

drop off a cliff; instead, the performance degraded gracefully. There was rapid recovery with

retraining using fewer neurons also. This shows the fault tolerance of neural networks.