Understanding Emotional Tone using NLP and ML Algorithms

Abstract: Emotional intelligence is pivotal in improving the interaction between humans and machines, especially for building AI systems that are user-friendly. This research proposes a new framework that enables Ōinteractive, emotion-sensitive communication between users and digital systems. It achieves this by integrating speech emotion detection with the generation of empathetic responses. Emotions like happiness, sadness, and anger are recognized using both acoustic signals (e.g., MFCC features and Librosa) and textual inputs. A deep learning setup utilizing CNN and LSTM networks ensures precise emotion recognition. Furthermore, the system uses advanced NLP models such as BERT and GPT to either generate or retrieve motivational quotes aligned with the detected emotional state. These responses are vocalized using text-to-speech tools to support natural verbal exchanges. The final implementation is an interactive web app that delivers emotionally intelligent assistance in real time. Test results indicate that the system performs effectively in recognizing emotions and delivering contextually appropriate feedback, showcasing its utility in domains like mental wellness and AI-driven personalization.