Directly Executable HMM-GMM Implementation with Speech Data
- Login to Download
- 1 Credits
Resource Overview
An efficient, ready-to-run HMM-GMM code implementation complete with speech datasets. This beginner-friendly example contains no technical barriers and provides clear insights into hidden Markov models combined with Gaussian mixture models for speech processing applications.
Detailed Documentation
This is a highly practical and immediately executable HMM-GMM code implementation that includes complementary speech data to facilitate better understanding for beginners. The code demonstrates fundamental HMM-GMM integration for speech pattern recognition, featuring straightforward configuration files and well-commented MATLAB/Python scripts. Key components include Baum-Welch algorithm implementation for HMM parameter estimation and EM algorithm for GMM training. This example maintains simplicity throughout without compromising technical accuracy, making it ideal for beginners to download and experiment with confidently. The package includes pre-processed feature extraction modules (MFCC), model initialization routines, and comprehensive documentation to ensure smooth execution.
- Login to Download
- 1 Credits