Speech To Tone And Emotion Analyzer

Speech-based emotion analysis has become increasingly important in understanding human behavior during spoken interactions, especially in customer service environments.This work presents a Speech-to-Tone and Emotion Analyzer designed to identify emotional states from speech signals using acoustic and prosodic characteristics.The system focuses on extracting key speech features such as pitch, energy, and spectral attributes to capture variations in tone and emotional expression.Machine learning techniques are employed to classify emotions effectively across different speech samples.By automating emotion detection, the proposed approach aims to support quality assurance and performance evaluation in call center operations.The model enhances real-time monitoring by providing insights into customer sentiment and agent behavior.Experimental evaluation demonstrates that speech signals contain sufficient emotional information to enable reliable emotion recognition, making the system a practical tool for emotion-aware customer interaction analysis.