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; calculating a sample-adaptive window bandwidth for each training token; determining a value for a loss function based on the computed scores and the calculated sample-adaptive window bandwidth for each training token; updating parameters in the current acoustic model to create a revised acoustic model based upon the loss value; and outputting the revised acoustic model.