Statistical Analysis of Voice Based Emotion Recognition using Similarity Measures

Human error is a mark assigned to an event that has negative effects or does not produce a desired result, with emotions playing an important role in how humans think and behave. If we detect feelings early, it may decrease human error. The human voice is one of the most powerful tools that can be used for emotion recognition. This study aims to reduce human error by building a system that detects positive or negative emotions of a user like (happy, sad, fear, and anger) through the analysis of the proposed vocal emotion component using Convolutional Neural Networks. By applying the proposed method to an emotional voice database (RAVDESS) using Librosa for voice processing and PyTorch, with the emotion classification of (happy/angry), the results show a better accuracy (98%) in comparison to the literature with regard to making a decision to deny or allow a user to access sensitive operations or send a warning to the system administrator prior to accessing system resources.

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Statistical Analysis of Voice Based Emotion Recognition using Similarity Measures

Cited by 3 publications

References 4 publications

An Entropy-Based Computational Classifier for Positive and Negative Emotions in Voice Signals

An Entropy-Based Computational Classifier for Positive and Negative Emotions in Voice Signals

A Study on Cross-Lingual Speech Emotion Analysis using Natural Language Processing

Using Vocal-Based Emotions as a Human Error Prevention System with Convolutional Neural Networks

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