Ravikrindhi Venkata Vignesh

Department of Computer Science and Engineering Kalasalingam Academy of Research and education

Title: Speech Emotion Analysis Using NLP Techniques and Deep Learning with MFCC Features

Abstract

Speech Emotion Analysis helps machines better understand people by recognizing emotions from spoken audio. This project uses a neural network model that analyzes audio features called Mel Frequency Cepstral Coefficients (MFCCs) to identify emotions like happiness, sadness, anger, fear, and neutrality. A web API, built using Flask, allows users to upload audio files and receive emotion predictions instantly, making the system practical for real-time use. The trained model, saved as speech_emotion_model.h5 and .keras, was developed using supervised learning techniques on labeled audio data. To ensure the model understands different emotion categories, a label encoder (label_encoder. pl) converts these categories into a numerical format that the model can interpret. The combination of efficient data processing and reliable predictions makes the system easy to integrate into other applications, such as virtual assistants or mental health tools. This paper outlines the complete process, including data preprocessing, feature extraction, model training, and evaluation. By enabling machines to detect emotions, the project demonstrates how speech emotion analysis can improve human-computer interaction and create more responsive, empathetic technologies.

Keywords—Speech Emotion Recognition, Neural Network, MFCC, Flask API, Real-time Emotion Detection, Label Encoding, Audio Processing, Human-Computer Interaction, Emotion Classification, Model Prediction.

Ravikrindhi Venkata Vignesh
Department of Computer Science and Engineering

Kalasalingam Academy of Research and education

Krishnankoil,India
vigneshravikrindi05@gmail.com

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