ASER: An Exhaustive Survey for Speech Recognition based on Methods, Datasets, Challenges, Future Scope.

Autor:	Patel, Dharil, Amipara, Soham, Sanaria, Malay, Pareek, Preksha, Jayaswal, Ruchi, Patil, Shruti
Předmět:	SPEECH perception DEEP learning MACHINE learning EMOTIONS TRANSFORMER models HUMAN-artificial intelligence interaction
Zdroj:	Revue d'Intelligence Artificielle; Apr2024, Vol. 38 Issue 2, p551-558, 8p
Abstrakt:	AI has been used to process the data for decision-making, problem-solving, interaction with humans and to understand human's feelings, emotions and their behavior. In today's world, communication between humans takes place digitally, so human's emotions play a very important role for communication as well as detection and analysis. Although there are many surveys related to emotions from speech already done, selecting appropriate datasets and methods are challenging tasks. This survey will primarily concentrate on efficient techniques, including Machine Learning, Deep Learning, and transformer-based approaches, while also providing brief descriptions of existing challenges and outlining future prospects. Additionally, this paper provides a comparative analysis of various datasets and techniques employed by researchers. After conducting the survey, we discovered that deep learning and transformer-based techniques are more effective and yield superior performance results. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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