Currently, there is a significant focus on natural language processing (NLP) within academic circles. As one of the initial domains of inquiry in the domain of machine learning, it has been utilized in a variety of significant sub-disciplines, such as text processing, speech recognition, and machine translation. Natural language processing has contributed to notable progress in computing and artificial intelligence. The recurrent neural network serves as a fundamental component for numerous techniques in domain of NLP. The present article conducts a comprehensive evaluation of various algorithms for processing textual and voice data, accompanied by illustrative instances of their functionality. Various algorithmic outcomes exhibit the advancements achieved in this field during the preceding decade. Our endeavor involved the classification of algorithms based on their respective types and expounding on the scope for future research in this domain. Furthermore, the study elucidates the potential applications of these heterogeneous algorithms and also evaluates the disparities among them through an analysis of the findings. Despite the fact that natural language processing has not yet achieved its ultimate objective of flawlessness, it is plausible that with sufficient exertion, the field will eventually attain it. Currently, a wide variety of artificial intelligence systems use natural language processing algorithms to comprehend human-spoken directions.
Keywords
Natural Language Processing, Machine Learning, Speech Recognition, Machine Translation, Text Processing.
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Matt Bowden
Matt Bowden
Computer Science and Engineering, Australian National University, Australia.
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Cite this article
Matt Bowden, “A Review of Textual and Voice Processing Algorithms in the Field of Natural Language Processing”, Journal of Computing and Natural Science, vol.3, no.4, pp. 194-203, October 2023. doi: 10.53759//181X/JCNS/202303018.
