Emotion Detection from Speech: A Comprehensive Approach Using Speech-to-Text Transcription and Ensemble Learning

Abstract

The field of text-to-emotion analysis is investigated in this study, which uses an interactive methodology to reveal subtle emotional insights in textual data. The research explores the complex relationship between language and emotion using sophisticated methods without focusing on any particular frontend or backend technology. The research attempts to improve our understanding of how literary information transmits emotional subtleties by emphasizing a broad but methodical examination. The lack of mentions of particular libraries and backends indicates an emphasis on the general ideas and techniques used in text-to-emotion analysis. The findings demonstrate the possibility of deriving significant emotional context from text, opening doors for applications in a variety of fields where user sentiment analysis is essential. This study adds to the body of knowledge on emotional intelligence in computational linguistics and lays the groundwork for future developments in text analysis techniques.