C++ Neural Networks and Fuzzy Logic: Preface

Application to Speech Recognition

Kohonen created a phonetic typewriter by classifying speech waveforms of different phonemes of Finnish

speech into different categories using a Kohonen SOM. The Kohonen phoneme map used 50 samples of each

phoneme for calibration. These samples caused excitation in a neighborhood of cells more strongly than in

other cells. A neighborhood was labeled with the particular phoneme that caused excitation. For an utterance

of speech made to the network, the exact neighborhoods that were active during the utterance were noted, and

for how long, and in what sequence. Short excitations were taken as transitory sounds. The information

obtained from the network was then pieced together to find out the words in the utterance made to the

network.

C++ Neural Networks and Fuzzy Logic:Preface

Variations and Applications of Kohonen Networks

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Summary

In this chapter, you have learned about one of the important types of competitive learning called Kohonen

feature map. The most significant points of this discussion are outlined as follows:

•  The Kohonen feature map is an example of an unsupervised neural network that is mainly used as

a classifier system or data clustering system. As more inputs are presented to this network, the

network improves its learning and is able to adapt to changing inputs.