EmoSpeech: Detecting Emotions from Spoken Language

Abstract

The crucial area of emotion detection from spoken language is explored in this research paper, which acknowledges the crucial part that emotions play in human communication and its implications for a variety of applications, including sentiment analysis, mental health monitoring, and human-computer interaction. The goals of the study include an examination of current emotion detection techniques, the development of a solid dataset for model testing and training, a thorough investigation of linguistic, prosodic, and auditory cues in emotional expression, the use of machine learning and deep learning models for emotion classification, and a careful evaluation of model performance, highlighting potential difficulties. The subjectivity and complexity of emotional expressions are highlighted by key findings, as well as the improved accuracy attained by combining various features, the superiority of deep learning models over conventional methods, the dataset's significant impact on model performance, and the promising applications in mental health monitoring and emotion-aware human-computer interaction. In essence, this study contributes to the field of emotion detection from spoken language by providing fresh perspectives, highlighting the value of various datasets, and emphasising real-world applications in emotional health and humancomputer interaction. It also lays the groundwork for future developments in the comprehension and use of emotions in spoken language analysis.