A Novel Framework for Speech-Based Detection of Schizophrenia using Machine Learning 
Judy Flavia B1, D. V. Vishnu Vardhan2, G. Arvind Goud3
1Judy Flavia B* , Asst. Professor at SRM Institute of Science and Technology, Ramapuram, Department of Computer Science and Engineering.
2D. V. Vishnu Vardhan, Student at SRM Institute of Science and Technology, Ramapuram, Department of Computer Science and Engineering.
3G. Arvind Goud, Student at SRM Institute of Science and Technology, Ramapuram, Department of Computer Science and Engineering.
Manuscript received on April 05, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 237-239 | Volume-9 Issue-4, April 2020. | Retrieval Number: D6756049420/2020©BEIESP | DOI: 10.35940/ijeat.D6756.049420
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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: Schizophrenia is a severe mental disorder that affects a person’s thoughts, feelings and behavior. The disorder thus has serious impact on a person’s personal and professional life. Traditionally the detection of schizophrenia is so far done with Electro Encephalography and MRI scans which make use of probabilistic methods. These methods are only useful when certain symptoms of the disorder are found. For early detection, a good method would be to use speech-based document using Conditional Random Fields algorithm. This method will use tagging of various speech components.
Keywords: Conditional Random Fields (CRF), Electro Encephalography (EEG ), Magnetic Resonance Imaging (MRI), Schizophrenia.