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This paper analyzes the capability of multilayer perceptron frontends to perform speaker normalization. We find the context decision tree to be a very useful tool to assess the speaker normalization power of different frontends. We introduce a gender question into the training of the phonetic context decision tree. After the context clustering the gender specific models are counted. We compare this for the following frontends: (1) Bottle-Neck (BN) with and without vocal tract length normalization (VTLN), (2) standard MFCC, (3) stacking of multiple MFCC frames with linear discriminant analysis (LDA). We find the BN-frontend to be even more effective in reducing the number of gender questions than VTLN. From this we conclude that a Bottle-Neck frontend is more effective for gender normalization. Combining VTLN and BN-features reduces the number of gender specific models further.