Look into the Fact Extraction and VERification (FEVER) workshop :)

Look into Convolutional Neural Networks as your architecture type and different types of spectrograms as your input features. The different layers of the CNN should do the feature transformation, and your final layer should be dense, with a softmax (or any other desired) activation function.

I'm assuming you're using sklearn for LinearRegression. You're initializing an instance of the LinearRegression class with a normalize parameter, but this is not valid for this class (for a list of possible parameters, see the documentation).

I'm not sure what you're trying to do, but I think you want to normalize your input data? In that case you should ook at MinMaxScaler. This transforms your features by scaling each feature to a given range.