Speech Emotion Recognition

Project Code :TCPGPY1930

Objective

To develop a robust end-to-end deep learning framework for Speech Emotion Recognition using raw audio inputs, leveraging R-CNN, Conformer Transformer, LSTM, and RNN to enhance emotion detection accuracy and generalization.

Abstract

This project delves into advancing Emotion Recognition in Speech through the utilization of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. By integrating these advanced techniques into speech processing, we aim to enhance the interpretation of human emotions from speech signals. Our objective is to refine human-computer interaction, with potential applications spanning mental health support and customer service analytics. The system harnesses CNN and LSTM architectures to extract features such as Mel-frequency cepstral coefficients (MFCC) and Chromogram for the precise classification of speech into emotional categories. Key stages of the project include data collection, feature extraction, emotion classification, model evaluation, real-time implementation, and user interface development. Challenges encompass navigating the nuanced nature of human emotions, accommodating speech pattern variations, and ensuring real-time processing capabilities. Through this endeavor, we endeavor to significantly contribute to the evolution of speech emotion recognition systems.

Keywords: Convolutional Neural Networks (CNN), Mel-frequency cepstral coefficients (MFCC), Long Short-Term Memory (LSTM).

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.