Hybrid DNN-HMM model for isolated digit recognition

Python implementation of a hybrid DNN-HMM models for isolated digit recognition.

Forced alignments are obtained from a GMM-HMM model and used to train the DNN. The DNN is a simple multi-layer perceptron (MLP) implemented using scikit-learn.

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 (mlp, hmm). Default: mlp
• --niter: Number of iterations to train the HMM. Default = 10
• --nstate: Number of states in HMM model. Default = 5
• --nepoch: Maximum number of epochs for training the MLP. Default=10
• --lr: Learning rate for the MLP. Default=0.01
• --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].