This project was a speech processing course exercise in my university. It uses HMM (hidden markov model) to recognize speech.
1.First you need some voice samples with .wav format for every word you want to recognize. Create a folder
named dataset (you can change folder names in constants) in the project root directory. Create a folder for each word
and put your samples inside that folder. For instance if you have two words like music and telegram, then you must
create a folder within dataset folder and put your voice samples inside folders named
music and telegram.
2.Now you need to run train.py to start the training process. It will look for folders in dataset and create a model
for each word inside another folder called models. Under the hood, it will extract features of every sample using
mfcc, mfcc delta, mfcc delta-delta, zero cross rating and energy.
3.Create a folder named recognize. Finally, you can run recognize.py to
start recognition process. Just copy your voice sample inside the folder.