Abstract
Learning is an essential part of any intelligent system and it is an inherent property in Artificial Neural Network (ANN) models. Recently, artificial neural network models have begun to emerge as powerful tools for learning, and for recognizing patterns with great variability similar to speech patterns. In the past, expert systems proved to be the most promising tools to handle highly variable data. During previous work in this area, we have developed a speech recognition system that uses certain expert system principles. Here, we describe an unsupervised learning method that is used to learn speech signal properties from a speech image such as a spectrogram.
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