Several methods have been proposed recently for modeling posterior representations derived from local classifiers [1, 2]. In recent work, Sainath et al. have proposed the use of a tied-mixture-based posterior modeling approach [3] to enhance exemplar-based posterior representations for phone recognition tasks. In this work, we conduct a detailed evaluation to determine the effectiveness of this technique on three representative posterior systems. In addition, we propose and evaluate an alternative discriminative formulation of the posterior modeling objective function that seeks to minimize framelevel errors. In experimental evaluations on the TIMIT corpus, we find that posterior modeling results in relative phone error rate (PER) reductions of between 1.1-5.5% across the systems tested. In fact, using S_pif-NN [4, 3] posteriors, we are able to achieve a PER of 18.5; to the best of our knowledge, this is the best result reported in the literature to date. minimize framelevel errors.