Enhanced Emotional Speech Analysis Using Recurrent Neural Network

In an era of increasing digital communication, this application meets a critical demand for accurate, automated emotion analysis in fields such as psychology, social sciences, marketing, and customer feedback. By employing a deep learning model trained on an annotated dataset of diverse emotional expressions, the application delivers a reliable and efficient emotion classification solution. Users can easily input text or audio for analysis via a user-friendly web interface. Audio input is first transcribed to text, after which all text undergoes preprocessing to enhance recognition accuracy by minimizing noise. The system displays predicted emotions intuitively, offering insights into the emotional undertones within the text. Its high accuracy, flexible design, and compatibility with audio input make the application a powerful resource for understanding complex emotional patterns in language, contributing both academic and practical value across sectors and advancing the capabilities of digital emotion analysis tools. The Emotion Classification Web Application, developed with Python and Flask, provides an advanced tool for analyzing and classifying emotions from both typed and audio-converted text inputs, achieving a high accuracy of 96.75% using a Recurrent Neural Network (RNN) model with Long Short-Term Memory (LSTM) capabilities.