A Novel Approach to Speech Recognition by Using Generalized Regression Neural Networks

• Published in: International journal of computer science issues Vol. 8; no. 2; p. 484
• Main Authors: Revada, Lakshmi Kanaka Venkateswarlu, Rambatla, Vasantha Kumari, Ande, Koti Verra Nagayya
• Format: Journal Article
• Language: English
• Published: Mahebourg International Journal of Computer Science Issues (IJCSI) 01.03.2011
• Subjects: Approximation; Classifiers; Computer science; Mathematical analysis; Mathematical models; Networks; Neural networks; Regression; Speech recognition
• ISSN: 1694-0814; 1694-0784

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.