I have been experimenting with speaker independent automatic speech recognition. At this point, I understand that the signal is typically segmented into overlapping frames and MFCCs are extracted as features for each frame.
A lot of the texts I read talk about reducing the dimensionality of the features using (for example) Linear Discriminant Analysis or Primary Component Analysis. While I think I understand the concepts involved in reducing dimensions in the abstract, I am not understanding how this feature reduction is supposed to be applied to the MFCC vectors.
I am interested primarily because several texts seem to suggest that reducing the dimensionality produces less variant features, which make recognizing patterns easier. As a bonus, there is also less computational overhead.
Is the dimension reduction supposed to be applied on a per-frame basis? For something like LDA, don't you need to know what class the data belongs to?