I've built a neural network for a speech recognition task using the timit dataset. I've extracted features using the perceptual linear prediction (PLP_ method. My features space has 39 dimensions (13 PLP values, 13 about first order derivative and 13 about second order derivative).
I would like to improve my dataset. The only thing I've tried thus far is normalizing the dataset using a standard scaler (standardizing features with mean 0 and variance 1).
My questions are:
- Since my dataset has high dimensionality, is there a way to visualize? For now, I've just plotted the dataset values using a heat map.
- Are there any methods for separating my sample even more, making it easier to differentiate between the classes?
My heat map is below, representing 20 samples. In this heatmap there are 5 different phonemes, related to vowels, in particular, uh, oy, aw, ix, and ey. As you can see, each phoneme is not really distinguishable from the others. Does anyone know how could I improve it?