Simple GMM-HMM model for isolated digit recognition

Python implementation of simple GMM and HMM models for isolated digit recognition.

This implementation contains 3 models:

1. Single Gaussian (sg): Each digit is modeled using a single Gaussian with diagonal covariance.
2. Gaussian Mixture Model (gmm): Each digit is modeled using a mixture of ncomp Gaussians, initialized by perturbing the sg model.
3. Hidden Markov Model (hmm): Each digit is modeled by an HMM consisting of nstate states, where the emission probability of each state is a single Gaussian with diagonal covariance.

How to run

python3 submission.py <opt-args> train test

• train is the training data
• test is the test data

The optional arguments are:

• --mode: Type of model (sg, gmm, hmm). Default: sg
• --niter: Number of iterations. Default = 10
• --ncomp: Number of components in GMM model. Default = 8
• --nstate: Number of states in HMM model. Default = 5
• --debug: Uses only top 100 utterances for train and test

Training data format

I cannot upload the full training and test data (for copyright reasons), but a small sample of the training data can be found at this Google Drive link. This should help in understanding the format of the data.

Help

This code is based on a template provided by Shinji Watanabe (Johns Hopkins University), written for a course project.

For assistance, contact [email protected].