Speech Emotion Detection System

Abstract

Human communication involves both verbal and non-verbal cues, where vocal tone and speech patterns play a significant role in expressing emotions. Identifying emotional states from speech can significantly enhance the quality of human-computer interaction. This project presents an emotion-aware chatbot system capable of detecting human emotions from speech input and generating contextually appropriate responses. The proposed system captures voice signals, extracts relevant acoustic features, and performs speech emotion recognition. The detected speech is then converted into text through a speech-to-text module. Based on the identified emotional state, the chatbot generates suitable text-based responses and provides relevant suggestions to the user. The system is developed using Python and integrates speech processing and natural language processing techniques to enable intelligent interaction. Experimental results demonstrate that incorporating emotion recognition improves personalization and responsiveness in conversational systems. The proposed approach contributes to the development of more adaptive and emotionally intelligent human-computer interfaces.