A Semantic Approach for Computing Speech Emotion Text Classification Using Machine Learning Algorithms

Abstract

The speech emotion is a critical human communication, which unquestionably involves a high level of happiness or sadness communication between people contacts. The sentimental feeling varies in significant proportions among different languages across the other regions over the world. The recognition of emotional states is a reasonably new method in the field of machine learning and AI. The paper presents the study and the performance results of a system for emotion taxonomy. The emotion can be expressed in ways that can be seen, such as makeover terminologies. The analyses on the Autism spectrum disorder(ASD) recorded voice data set are converted into text data. However, in this research paper, we are interested in detecting emotions from the various textual dataset as well as using semantic data augmentation process to fill a few of the words, sentences, or half-broken words, as the Autism spectrum disorder (ASD) patients lack the social communication skills, as the patient does not very well articulate their communication.