A Novel Approach to Speech Recognition by Using Generalized Regression Neural Networks
Speech recognition has been a subject of active research in the last few decades. In this paper, the applicability of a special model of Generalized Regression Neural Networks as a classifier is studied. A Generalized Regression Neural Network (GRNN) is often used for function approximation. It has a radial basis layer and a special linear layer. This network uses a competitive function for computing final result. The proposed network has been tested on one digit numbers dataset and produced significantly lower recognition error rate in comparison with common pattern classifiers. All of classifiers use Linear Predictive Cepstral Coefficients and Mel - Frequency Cepstral Coefficients. Results for proposed network shows that LPCC features yield better performance when compared to MFCC. It is found that the performance of Generalized Regression Neural Networks is superior to the other classifiers namely Linear and Multilayer Perceptron Neural Networks.
Keywords: Cepstral Coefficients, Linear Predictive Cpestral Coefficients, Mel –Frequency Cpestral Coefficients Linear Neural Networks, Multilayer Perceptrons, Generalized Regression Neural Networks, Classifiers
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