Malay isolated speech recognition using neural network : a work in finding number of hidden nodes and learning parameters

Abstract EN

This paper explains works in speech recognition using neural network.

The main objective of the experiment is to choose suitable number of nodes in hidden layer and learning parameters for Malay isolated digit speech problem through trial and error method.

The network used in the experiment is feed forward multilayer perceptron trained with back propagation scheme.

Speech data for the study are analyzed using linear predictive coding and log area ratio to represent speech signal for every 20ms through a fixed overlapped windows.

The neural network learning operation is greatly influenced by the parameters i.e., momentum, learning rate and number of hidden nodes chosen.

The result shows that choosing unsuitable parameters lead to unlearned network while some good parameters set from previous work perform badly in this application.

Best recognition rate achieved was 95% using network topology of input nodes, hidden nodes and output nodes of size 320:45:4 respectively while the best momentum rate and learning rate in the experiment were 0.5 and 0.75.