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Genesis of English Language with its Cybernetics Origin

Reji M. Issac

Abstract


Abstract

This paper is looking into the genesis of the English language with its Cybernetics origins and hence looking into the possibilities of developing new techniques to be used in speech processing applications. As we all know, English is the third-most-spoken language in the world by a number of native speakers, which is used to create the present world, which is also a West Germanic language that was first spoken early medieval England, and is now a global lingua franca (Bridge language). The analysis to synthesis power of this language about any matter ranging from tangible to intangible, first order cybernetics to second order cybernetics is based on the basic structure of the English language as described in this paper, as a language with Twenty-Eight alphabets with Alpha in the beginning and Omega in the end, together constituting Symbol of Word connected with lemniscus, along with the singularity. This paper is trying bring out how the English words can be processed, generated, analyzed, synthesized, and how it can be used for prediction, using Logical Symbol of Cybernetics and Symbol of Word, in a modern data-driven society. Also when the activities in computers with artificial intelligence are designed for including Speech recognition and Learning, this paper gives an optimum solution in machine learning and AI by providing a new model in speech signal processing.

Keywords: Biolinguistics, Evolutionary linguistics, Cybernetics, Cognition, Cognitive Science, Language, English, Linguistics, Clinical linguistics, Computational linguistics, Forensic linguistics, Speech Processing, Natural-Language Processing, Biosemiotics, Micro linguistics, Neurolinguistics, psycholinguistics, Cognitive psychology, Data Science, Speech Analysis, Speech Synthesis, Speech Recognition, Spoken Language Processing, Computational Cybernetics

Cite this Article

Reji M. Issac. Genesis of English Language with its Cybernetics Origin. Current Trends in Signal Processing. 2020; 10(1): 19–28p.


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DOI: https://doi.org/10.37591/ctsp.v10i1.3962

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