1. A method of training an acoustic model in a speech recognition system, comprising: utilizing a training corpus, having training tokens, to calculate an initial acoustic model; computing, using the initial acoustic model, a plurality of scores for each training token with regard to a correct class and a plurality of competing classes; utilizing a symmetric kernel function that is based on an exponent of the plurality of scores to calculate a sample-adaptive window bandwidth for each training token; utilizing a loss function to calculate a margin for each training token; gradually increasing the margin for each training token over a number of iterations until a minimum word error rate is achieved; determining derivatives of the loss function based on the computed scores, based on the calculated sample-adaptive window bandwidth for each training token, and based on the iteratively increased margin for each training token; calculating a Bayes risk value that includes a margin-free Bayes risk component and a margin-bound Bayes risk component, the margin-free Bayes risk component being based on an integral computed from zero to infinity, and the margin-bound Bayes risk component being based on an integral computed from a negative value of a discriminative margin to zero; updating parameters in the initial acoustic model to create a revised acoustic model based upon the derivatives of the loss function and the Bayes Risk value; and outputting the revised acoustic model.