Speech Recognition Project

📌 Project Overview

This project focuses on speech recognition using Hidden Markov Models (HMMs). The goal is to process audio data and train a model to recognize speech patterns efficiently.

🛠️ Technologies Used

• Programming Language: Python
• Libraries:
  • numpy - Numerical computations
  • matplotlib - Data visualization
  • wave - Audio file handling
  • multiprocessing - Parallel processing
  • hmmfunc - Hidden Markov Model implementation
  • MonoPhoneHMM - Monophone-based HMM

⚙️ Installation

To set up the environment, install the required dependencies:

pip install numpy matplotlib hmmfunc

📂 Project Structure

📁 Speech_Recognition_Project
│── main.ipynb   # Jupyter Notebook with full implementation
│── data/        # Directory for audio files
│── models/      # Trained HMM models
│── scripts/     # Additional processing scripts
└── README.md    # Project documentation

🔍 Results & Analysis

• The model is trained using HMMs for speech recognition.
• Performance is evaluated using accuracy metrics.
• Future improvements include neural network integration for better accuracy.

📌 Future Work

• Implement deep learning models like CNNs or RNNs for better accuracy.
• Experiment with different feature extraction methods like MFCC.
• Enhance noise robustness for real-world scenarios.

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📢 Contributions are welcome! Feel free to open an issue or submit a pull request.