Abstract
Abstract: This project proposes a machine learning basedapproach to predict the sentiment i.e Stressed, Unstressed orNeutral from voice of a human being and also presents a comparative analysis among various machine learning models. This technology seeks to distinguish between stressed and non-stressed outputs in response to stimuli. The dataset used fortraining in this project comprises audio recordings based onfour different situations, each recorded in various emotionalstates. Given the increased demand for communication betweenintelligent systems and human, automatic stress detection isbecoming an interesting research topic. Even while the level ofspecific hormones, such cortisol, can be precisely measured to indicate stress, this approach is not feasible for diagnosing stress in interactions between humans and machines. The Sentiment Predictor for Stress Detection Using Voice Stress Analysis utilizesmachine learning algorithms such as SVM, Random Forest , Logistic Regression etc. to analyze voice patterns and predict an individual’s emotional state based on the changes in their vocal characteristics. The technology can identify changes in pitch, tone, and rhythm, among other factors, to determinean individual’s stress level. Overall, the Sentiment Predictorfor Stress Detection Using Voice Stress Analysis is a promising technology that can provide valuable insights into an individual’s emotional state and support their well-being in various settings
Published Version
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