A Deep Learning-Based Speech Emotion Recognition Framework

This repository contains a deep learning model for Speech Emotion Recognition (SER) using LSTM layers. The model is designed to classify speech recordings into various emotions, including fear, angry, happy, sad, disgust, neutral, and pleasant surprise. The project utilizes Natural Language Processing (NLP) techniques and deep learning to achieve this classification.

Overview

Speech Emotion Recognition is a vital area in the field of natural language processing and human-computer interaction. This project demonstrates how to build a deep learning model to recognize emotions from speech recordings. The model employs LSTM layers for sequence modeling, allowing it to capture temporal dependencies in the audio data.

• Code: The Code folder contains the complete implementation of the deep learning model using LSTM layers. The code is organized and well-commented to facilitate understanding and modification. The main script or notebook in this directory serves as the entry point for understanding the model architecture, training process, and prediction.

• Dataset The Dataset folder holds the speech recordings dataset used to train and evaluate the emotion recognition model. The dataset is structured to include labeled examples for various emotions such as fear, anger, happiness, sadness, disgust, neutral, and pleasant surprise. If you wish to replicate or extend the project, you can explore the dataset and ensure proper attribution as per the dataset's licensing terms.

• Model In the Model folder, you'll find the pre-trained model weights and architecture. This allows you to use the trained model for prediction without having to retrain it from scratch. If you decide to fine-tune or modify the model, the code in the Code folder will guide you through the process.

• Result The Result folder contains the output and evaluation results from training the model. This includes metrics, graphs, and any other relevant information that provides insights into the model's performance. You can use this information to assess the effectiveness of the implemented Speech Emotion Recognition system.

Libraries

To use this project, you will need to have the following libraries installed:

• librosa

• seaborn

• matplotlib

• scikit-learn

• pandas

• IPython

• numpy

• keras

Dataset

The dataset used for this project is the TESS(Toronto emotional speech set). It can be found on Kaggle at the following link: TESS Dataset. It contains speech recordings labeled with emotions. The emotions and their distribution in the dataset are as follows:

• Fear: 400 samples

• Angry: 400 samples

• Happy: 400 samples

• Sad: 400 samples

• Disgust: 400 samples

• Neutral: 400 samples

• Pleasant Surprise: 400 samples

The dataset is a multiclass classification problem, where the goal is to classify speech recordings into one of these seven emotion categories.

Model Architecture

The deep learning model used for Speech Emotion Recognition is a sequential model with the following layers:

• LSTM layers for sequence modeling

• Dense layers for classification

• Dropout layers for regularization

This architecture is designed to capture both spatial and temporal features in the audio data.

Training

The model was trained on the provided dataset, and the training results are as follows:

Accuracy

• Training accuracy: 98%

• Test accuracy: 96%

Loss

• Training loss: 0.06

• Test loss: 0.1

The model is capable of accurately recognizing emotions in speech recordings.

Contributing

If you would like to contribute to the project, follow these steps:

• Fork the repository.
• Create a new branch for your feature or bug fix.
• Make your changes and submit a pull request.