![]() ![]() The results reveal that the maximum accuracy of 89% is achieved when the depth of the hidden layer is 42. The depth of the layer is chosen as 20, 42 and 60 and the accuracy of each system is determined. A vocabulary of 80 words which constitute 20 sentences is used. There are five layers namely, an input layer, a fully connected layer, a hidden LSTM layer, SoftMax layer and a sequential output layer. The design process involves speech acquisition, pre-processing, feature extraction, training and pattern recognition tasks for a spoken sentence recognition system using LSTM-RNN. 1/2/3, 2020Ībstract:The purpose of this paper is to design an efficient recurrent neural network (RNN)-based speech recognition system using software with long short-term memory (LSTM). International Journal of Intelligent Enterprise (IJIE), Vol. Recurrent neural network-based speech recognition using MATLABīy Praveen Edward James Mun Hou Kit Chockalingam Aravind Vaithilingam Alan Tan Wee Chiat ![]()
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