Neutrosophic speech recognition Algorithm for speech under stress by Machine learning

Abstract

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.