No abstract

Recognizing emotions and human speech has always been an exciting challenge for scientists. In our work the parameterization of the vector is obtained and realized from the sentence divided into the containing emotional-informational part and the informational part is effectively applied. The expressiveness of human speech is improved by the emotion it conveys. There are several characteristics and features of speech that differentiate it among utterances, i.e. various prosodic features like pitch, timbre, loudness and vocal tone which categorize speech into several emotions. They were supplemented by us with a new classification feature of speech, which consists in dividing a sentence into an emotionally loaded part of the sentence and a part that carries only informational load. Therefore, the sample speech is changed when it is subjected to various emotional environments. As the identification of the speaker’s emotional states can be done based on the Mel scale, MFCC is one such variant to study the emotional aspects of a speaker’s utterances. In this work, we implement a model to identify several emotional states from MFCC for two datasets, classify emotions for them on the basis of MFCC features and give the correspondent comparison of them. Overall, this work implements the classification model based on dataset minimization that is done by taking the mean of features for the improvement of the classification accuracy rate in different machine learning algorithms. In addition to the static analysis of the author's tonal portrait, which is used in particular in MFFC, we propose a new method for the dynamic analysis of the phrase in processing and studying as a new linguistic-emotional entity pronounced by the same author. Due to the ranking by the importance of the MEL scale features, we are able to parameterize the vectors coordinates be processed by the parametrized KNN method. Language recognition is a multi-level task of pattern recognition. Here acoustic signals are analyzed and structured in a hierarchy of structural elements, words, phrases and sentences. Each level of such a hierarchy may provide some temporal constants: possible word sequences or known types of pronunciation that reduce the number of recognition errors at a lower level. An analysis of voice and speech dynamics is appropriate for improving the quality of human perception and the formation of human speech by a machine and is within the capabilities of artificial intelligence. Emotion results can be widely applied in e-learning platforms, vehicle on-board systems, medicine, etc

In the context of information technology governance (ITG), this study delves into the connection between political, rational and technological approaches in the decision-making process (DMP) and their influence on enterprise governance. The primary objective is to explore the interdependence of these approaches and assess their impact. The study employs a literature review that analyzes the relationship among rationality, politics, and technology, including artificial intelligence (AI), machine learning, and business intelligence. The research addresses the question of how these various techniques can be integrated into the decision-making process. It also provides a theoretical framework for implementing each of these models. The authors propose that situations involving AI, rationality, or politics are the basis for the decision-making process. After proceeding with a literature review, a correlation between the actions of certain managers who used Big Data and machine learning in their decision-making process and rationality has been found. However, the research did not find any correlation between these commitments and political models. In conclusion, this study highlights the importance of understanding the interplay between rational and political approaches in the decision-making process within ITG.