Recurrent neural network-based speech recognition using MATLAB

Praveen Edward James, Mun Hou Kit, Chockalingam Aravind Vaithilingam and Alan Tan Wee Chiat

International Journal of Intelligent Enterprise, 2020, vol. 7, issue 1/2/3, 56-66

Abstract: 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). The design process involves speech acquisition, pre-processing, feature extraction, training and pattern recognition tasks for a spoken sentence recognition system using LSTM-RNN. There are five layers namely, an input layer, a fully connected layer, a hidden LSTM layer, SoftMax layer and a sequential output layer. A vocabulary of 80 words which constitute 20 sentences is used. The depth of the layer is chosen as 20, 42 and 60 and the accuracy of each system is determined. The results reveal that the maximum accuracy of 89% is achieved when the depth of the hidden layer is 42. Since the depth of the hidden layer is fixed for a task, increased performance can be achieved by increasing the number of hidden layers.

Keywords: speech recognition; feature extraction; pre-processing; recurrent neural network; RNN; long short-term memory; LSTM; hidden layer; MATLAB. (search for similar items in EconPapers)
Date: 2020
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