Abstract
A connected word recognition system which makes use of two neural network models, namely, Kohonen’s Network and a Multilayer Perceptron is implemented. The digitized speech signal is represented by a sequence of Linear Predictive Coding (LPC) coefficients and segmented into syllables. The Kohonen’s Network is used to perform vector quantization to compress LPC data for the input of the Multilayer Perceptron (MP). MP is used to perform recognition in syllable basis with the Back-Propagation algorithm used for training. The words are constructed by using the sequence of recognized syllables. The system was trained and tested with ten Turkish words with sixteen syllables, with an overall recognition rate of 90 percent.
Published Version
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