Neutrosophic speech recognition Algorithm for speech under stress by Machine learning

被引：0
作者：
Nagarajan D. [1 ]
Broumi S. [2 ,3 ]
Smarandache F. [4 ]
机构：
[1] Department of Mathematics, Rajalakshmi Institute of Technology, Tamil Nadu, Chennai
[2] Laboratory of Information Processing, Faculty of Science Ben M’Sik, University Hassan II, B.P 7955,Sidi Othman, Casablanca
[3] Regional Center for the Professions of Education and Training(C.R.M.E.F), Casablanca-Setat
[4] The University of New Mexico, Mathematics, Physics, and Natural Science Division, 705 Gurley Ave., Gallup, 87301, NM
来源：
Neutrosophic Sets and Systems | 2023年 / 55卷
关键词：
categorization of stress in speech; linguistic technology; Machine learning; Neutrosophic; speech recognition;
D O I：
10.5281/zenodo.7832714
中图分类号：
学科分类号：
摘要：
It is well known that the unpredictable speech production brought on by stress from the task at hand has a significant negative impact on the performance of speech processing algorithms. Speech therapy benefits from being able to detect stress in speech. Speech processing performance suffers noticeably when perceptually produced stress causes variations in speech production. Using the acoustic speech signal to objectively characterize speaker stress is one method for assessing production variances brought on by stress. Real-world complexity and ambiguity make it difficult for decision-makers to express their conclusions with clarity in their speech. In particular, the Neutrosophic speech algorithm is used to encode the language variables because they cannot be computed directly. Neutrosophic sets are used to manage indeterminacy in a practical situation. Existing algorithms are used except for stress on Neutrosophic speech recognition. The creation of algorithms that calculate, categorize, or differentiate between different stress circumstances. Understanding stress and developing strategies to combat its effects on speech recognition and human-computer interaction system are the goals of this recognition. © 2023, Neutrosophic Sets and Systems. All Rights Reserved.
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收藏
页码：46 / 57
页数：11
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