Features used: MFCC, extracted from https://github.com/jameslyons/python_speech_features
First 100 values across only one of the dimensions were used
The model submitted was a Logistic Regression one, with regularization c = 0.1 and l1 penalty (everything else was at default with the sklearn module).
The training set was around 70% of the data, and validation was 30%.