We present an approach to broad phonetic classification, defined as mapping acoustic speech frames into broad (or clustered) phonetic categories. Our categories consist of silence, general voiced, general unvoiced, mixed sounds, voiced closure, and plosive release, and are sufficiently rich to allow accurate time-scaling of speech signals to improve their intelligibility in, e.g. voice-mail applications. There are three main aspects to this work. First, in addition to commonly used speech features, we employ acoustic time-scale features based on the intra-scale relationships of the energy from different wavelet subbands. Secondly, we use and compare against discriminatively learned Bayesian networks. By this, we mean Bayesian networks whose structure and/or parameters have been optimized using a discriminative objective function. We utilize a simple order-based greedy heuristic for learning discriminative structure based on mutual information. Given an ordering, we can find the discriminative classifier structure with score evaluations (where q is the maximum number of parents per node). Third, we provide a large assortment of empirical results, including gender dependent/independent experiments on the TIMIT corpus. We evaluate both discriminative and generative parameter learning on both discriminatively and generatively structured Bayesian networks and compare against generatively trained Gaussian mixture models (GMMs), and discriminatively trained neural networks (NNs) and support vector machines (SVMs). Results show that: (i) the combination of time-scale features and mel-frequency cepstral coefficients (MFCCs) provides the best performance; (ii) discriminative learning of Bayesian network classifiers is superior to the generative approaches; (iii) discriminative classifiers (NNs and SVMs) perform better than both discriminatively and generatively trained and structured Bayesian networks; and (iv) the advantages of generative yet discriminatively structured Bayesian network classifiers still hold in the case of missing features while the discriminatively trained NNs and SVMs are unable to deal with such a case. This last result is significant since it suggests that discriminative Bayesian networks are the most appropriate approach when missing features are common.