HMM-GMM Implementation

I have carefully revised and thoroughly tested the program code to ensure its proper functionality. This implementation is particularly useful for beginners as it provides a practical foundation for understanding HMM-GMM concepts. The code includes detailed annotations and explanations for each section, covering key aspects such as: - Initialization of HMM parameters including state transitions and emission probabilities - Gaussian Mixture Model implementation for modeling observation distributions - Baum-Welch algorithm for parameter estimation and training - Forward-backward algorithm implementation for probability calculations - Viterbi algorithm for optimal state sequence decoding These enhancements make the code more accessible and educational, allowing learners to grasp both the theoretical concepts and practical implementation details of HMM-GMM systems. The modular structure enables easy experimentation with different parameter configurations and dataset applications.