Human-Computer Interaction with Special Emphasis on Converting Brain Signals to Speech
Venkatesh Sharma1, Kumar Sambhav Jain2, Amrita Kaur3, Ashima Singh4
1Venkatesh Sharma*, Student in Computer Engineering at Thapar institute of Engineering and Technology, Patiala.
2Kumar Sambhav Jain ,B. tech student in Computer Engineering at Thapar Institute of Engineering and Technology, Patiala.
3Amrita Kaur, Assistant Professor in the Computer Science and Engineering Department, at Thapar Institute of Engineering & Technology, Patiala.
4Ashima Singh, Assistant Professor in the Computer Science and Engineering Department, at Thapar Institute of Engineering & Technology, Patiala.
Manuscript received on March 15, 2020. | Revised Manuscript received on April 01, 2020. | Manuscript published on April 10, 2020. | PP: 523-529 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3650049620/2020©BEIESP | DOI: 10.35940/ijitee.F3650.049620
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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: Huge hurdle neuro engineers face on the road to effective brain-computer interfaces is attempting to translate the big selection of signals made by our brain into words pictures which may be simply communicable. The science-fiction plan of having the ability to manage devices or communicate with others simply by thinking is slowly but surely, obtaining nearer to reality. Translating brainwaves into words has been another large challenge for researchers, but again with the help of machine learning algorithms, superb advances are seen in recent years. The exploitation of deep learning and acceptable machine learning algorithms, the management signals from the brain will regenerate to some actions or some speech or text. For this, a neural network is created for the brain and conjointly a mapping is completed to catch all the brain signals in which neural network will be additionally used for changing these signals into actions. From the past literature, it is being concluded that the Deep Neural Networks are one of the main algorithms that are being placed into use for this research. This review article majorly focuses on studying the behavioral patterns generated by the brain signals and how they can be converted into actions effectively so that people suffering from semi or full paralysis can use this technology to live a normal life if not completely but to a certain extent. Also, it focuses on analyzing and drawing a comparison between linear and non-linear models and to conclude the best-suited model for the same currently available to the researchers.
Keywords: Brain, Human-Computer Interaction, Deep Neural Networks, Brain-Computer Interaction, Linear Auditory, Linear Vocoder, DNN Auditory, DNN Vocoder.
Scope of the Article: Advanced Computer Networking Computational Intelligence.