Modeling of Speech Recognition Using Artificial Neural Network
Dr. Nidhi Srivastava, Asst. Professor, Amity Institute of Information Technology, Amity University, Uttar Pradesh, Lucknow Campus, Lucknow, India.
Manuscript received on 28 June 2019 | Revised Manuscript received on 04 July 2019 | Manuscript published on 30 July 2019 | PP: 1225-1229| Volume-8 Issue-9, July 2019 | Retrieval Number: I7516078919/19©BEIESP | DOI: 10.35940/ijitee.J9833.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Automatic speech recognition has attained a lot of significance as it can act as easy communication link between machines and humans. This mode of communication is easy for man to use as it is effortless and easy. Many approaches for extraction of the features of the speech and classification of speech have been considered. This paper unveils the importance of neutral network and the way it can be used for recognition of speech. Mel Frequency Cepstrum Coefficients is made use of for extraction of the features from the voice. For pattern matching neural network has been used. MATLAB has been used to show how the speech is recognized. In this paper the speech recognition has been done firstly by multilayer feed forward neural network using Back propagation algorithm. Then the process of speech recognition is shown by using Radial basis function neural network. The paper then analyzes the performance of both the algorithms and experimental result shows that BPNN outperforms the RBFNN.
Keywords: BPNN, Feature Extraction, Neural Network, RBFNN, Speech Classification.
Scope of the Article: Computer Network