1. PROBLEM STATEMENT:
• To develop a robust and accurate system that
automatically identifies the emotional state of a speaker
from their speech signal using deep learning techniques.
• Speech Emotion Recognition is a challenging task in
human-computer interaction, aiming to accurately
identify emotions from speech signals.
• Detecting an emotion is only part of the solution;
translating that detection into meaningful and
personalized action recommendations remains a
significant hurdle.
• Enabling applications in human-computer interaction
where understanding the emotional context of
communication is crucial.
2. Objective :
• Develop an AI model to recognize emotions from speech
• Suggest actions based on detected emotions
• Enhance user experience and emotional well-being
• Provide real-time and personalized recommendations
• Improve human-computer interaction through emotion-aware responses
• Facilitate emotional intelligence in AI-driven systems
4. Overview :
• The project focuses on recognizing human emotions through speech signals
and providing action-based suggestions to enhance user well-being.
• It utilizes machine learning and deep learning techniques to classify emotions
such as happiness, sadness, anger, and neutrality.
• The system processes recorded speech, extracts relevant features, and
predicts the user's emotional state.
• Based on the detected emotion, the system provides personalized action
recommendations, such as listening to music, engaging in relaxation exercises,
or performing motivational tasks.
• The goal is to create an AI-powered emotional intelligence system that
improves human-computer interaction and supports mental well-being.
