Speech Recognition Using Elman Artificial Neural Network and Linear Predictive Coding

Background: Today, the automatic intelligent system requirement has caused an increasing consideration on the interactive modern techniques between human being and machine. These techniques generally consist of two types: audio and visual methods. Meanwhile, the need for developing the algorithms that enable the human speech recognition by machine is of high importance and frequently studied by the researchers. Objective: Using artificial intelligence methods has led to better results in human speech recognition, but the basic problem is the lack of an appropriate strategy to select the recognition data among the huge amount of speech information that practically makes it impossible for the available algorithms to work. Method: In this article, to solve the problem, the linear predictive coding coefficients extraction method is used to sum up the data related to the English digits pronunciation. After extracting the database, it is utilized to an Elman neural network to recognize the relation between the linear coding coefficients of an audio file with the pronounced digit. Results: The results show that this method has a good performance compared to other methods. According to the experiments, the obtained results of network training (99% recognition accuracy) indicate that the network still has better performance than RBF despite many errors. Conclusion: The results of the experiments showed that the Elman memory neural network has had an acceptable performance in recognizing the speech signal compared to the other algorithms. The use of the linear predictive coding coefficients along with the Elman neural network has led to higher recognition accuracy and improved the speech recognition system.