This repository contains a project on Speech Emotion Recognition using an audio dataset. The goal of this project is to recognize and classify emotions from speech signals. Emotion recognition from speech has applications in various fields, including human-computer interaction, call center analytics, and mental health monitoring.

The task of this project is to build a model that can analyze speech signals and accurately classify the corresponding emotions. Emotions can be classified into categories such as happy, sad, angry, neutral, etc. The project utilizes an audio dataset containing recordings of individuals expressing different emotions.

The audio dataset used for this project consists of speech recordings labeled with corresponding emotion categories. The dataset may include multiple speakers and a diverse range of emotions. It is important to ensure that the dataset is appropriately labeled and contains a sufficient number of samples for each emotion category.

The project can be divided into the following tasks:

Load the audio dataset and extract relevant features from the speech signals. Perform data cleaning and preprocessing to remove noise or artifacts that may affect the emotion recognition accuracy. Split the dataset into training and testing sets, ensuring a balanced distribution of emotion categories in both sets.

Extract relevant acoustic features from the speech signals, such as MFCC (Mel-frequency cepstral coefficients), pitch, intensity, etc. Consider using techniques like windowing, framing, and spectral analysis to capture temporal and spectral characteristics of the speech.

Choose a suitable machine learning or deep learning model for speech emotion recognition, such as Support Vector Machines (SVM), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), or a combination of these models. Train the selected model using the preprocessed audio dataset, using appropriate training techniques like mini-batch training or transfer learning if necessary. Fine-tune the model hyperparameters to achieve optimal performance.

Evaluate the trained model on the testing set to measure its performance in recognizing emotions accurately. Calculate relevant evaluation metrics such as accuracy, precision, recall, and F1 score to assess the model's performance. Generate a confusion matrix to analyze the model's ability to correctly classify different emotion categories.

Deploy the trained model to a real-time or batch processing system for emotion recognition on new or unseen speech signals. Develop an application or interface that allows users to interact with the deployed model and obtain emotion predictions from their speech.

Document the results obtained from the trained model, including the evaluation metrics and the confusion matrix. Discuss any challenges faced during the project, such as data preprocessing difficulties, class imbalance issues, or model performance limitations. Provide insights into the potential applications of the speech emotion recognition system and suggestions for future improvements.

Clone the repository:

Install the required dependencies mentioned in the project documentation.

Prepare the audio dataset: Ensure that the dataset is properly organized and labeled with corresponding emotion categories.

Perform data preprocessing: Use the provided scripts or modules to preprocess the audio data, including noise removal, feature extraction, and dataset splitting.

Implement the selected machine learning or deep learning model using the preprocessed audio dataset. Train the model on the training set.

Evaluate the trained model on the testing set and calculate relevant evaluation metrics.

Deploy the trained model to a real-time or batch processing system for emotion recognition on new or unseen speech signals.