File Descriptions:

train_models.py:

• Reading audio data of a speaker (5 utterances of speaker: belmontguy)
• Extracting MFCC features
• Training GMM model
• Dumping GMM model onto Local Storage(.gmm)

train_GMM_x.py (x=2:5):

• Reading audio data of a speaker (5 utterances of speaker)
• Extracting MFCC features
• Training GMM model
• Dumping GMM model onto Local Storage(.gmm)

test.py:

• Read audio files of 5 test speakers using Scipy
• Read the GMM model(.gmm) from local storage
• Test against all speakers
• Computer Winner speaker based on log_likelihood

delta_40_extraction.py:

• Read an audio(.wav) file.
• Extract MFCC and MFCC_Deltas.
• Horizontally stacked to form a 40 dimensional feature vector.

audio_splitting.py:

• Read the audio file(.flac) from SITW database
• Compute duration of all audio files
• Return all audio files with duration > 1000s
• Split the audio file (75% and 25%) for training and testing
• Write the split audio (.flac) files in the

sitw_train: *Load the training audio files from audio_splitting.py *Extract MFCC , train Model , dump to storage