VOICE TO TEXT EMOTION DETECTION SYSTEM FOR FEEDBACK ANALYSIS

The Voice to Text Emotion Detection System for Feedback Analysis is designed to analyze emotions from spoken feedback by converting audio into text and identifying underlying sentiments. The system follows a structured pipeline: it processes the audio input using signal processing techniques, extracts key audio features such as pitch, tone, energy, and speech rate, and labels the data for training. A deep neural network (DNN) classifier is then used to detect emotions, followed by performance evaluation to ensure accuracy. The results help in understanding user sentiment, which can be applied in various domains like customer feedback analysis, mental health monitoring, and human-computer interaction. This system enhances feedback analysis by providing deeper insights into user emotions beyond textual data alone.