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Dysarthric Speech Recognition

Project Overview

This project aims to classify dysarthric and non-dysarthric speech using deep learning techniques. The dataset consists of audio samples from dysarthric and non-dysarthric individuals, with the goal of developing a model that can accurately distinguish between the two classes.

Features

  • Speech waveplot visualization
  • Spectrogram analysis
  • Zero-crossing rate (ZCR) calculation and visualization
  • Feature extraction using MFCC
  • Deep learning model implementation using CNN

Data Information

The dataset contains 2000 audio samples divided into four categories:

  • Dysarthric females: 500 samples
  • Dysarthric males: 500 samples
  • Non-dysarthric females: 500 samples
  • Non-dysarthric males: 500 samples

Data CSV Information

The data.csv file contains the following columns:

  • filename: Path to the audio file
  • is_dysarthria: Indicates if the sample is from a dysarthric individual (dysarthria or non_dysarthria)
  • gender: Gender of the speaker (male or female)

Usage

  1. Clone the repository.
  2. Ensure the dataset is located in the specified directory.
  3. Run main.m to load data, visualize waveplots, spectrograms, and ZCRs, and train the CNN model.

Work in Progress

This project is a work in progress, and additional features and improvements are being actively developed.

License

This project is licensed under the MIT License.