17. A computing system comprising: a plurality of storage computing devices, each configured to store a respective set of reference transcriptions of reference utterances, and a respective set of feature vectors, and to have access to a weight vector, wherein the respective sets of reference transcriptions were derived using a strong acoustic model, a language model and the weight vector, and wherein each feature vector is pairwise associated with a reference utterance stored on the same storage computing device; a plurality of training computing devices each configured to select a respective partition of the reference utterances, wherein each reference utterance in the respective partition is associated with a respective confidence level of at least 70%, wherein each training computing device is configured to, based on the reference transcription having the confidence level of at least 70%, apply a weak acoustic model, the language model, and the weight vector to the of reference utterances in the respective partition to determine a set of respective secondary transcriptions, wherein the respective secondary transcriptions have respective secondary confidence levels, wherein the weak acoustic model has a higher error rate than the strong acoustic model, and wherein at least some respective secondary transcriptions are different from the respective reference transcriptions; and at least one combining computing device configured to, based on the feature vectors associated with the reference utterances of the selected partitions, update the weight vector so that transcribing the respective reference utterances using the weak acoustic model, the language model and the updated weight vector results in respective tertiary transcriptions with respective tertiary confidence levels that are greater than the respective secondary confidence levels.
17. A computing system comprising: a plurality of storage computing devices, each configured to store a respective set of reference transcriptions of reference utterances, and a respective set of feature vectors, and to have access to a weight vector, wherein the respective sets of reference transcriptions were derived using a strong acoustic model, a language model and the weight vector, and wherein each feature vector is pairwise associated with a reference utterance stored on the same storage computing device; a plurality of training computing devices each configured to select a respective partition of the reference utterances, wherein each reference utterance in the respective partition is associated with a respective confidence level of at least 70%, wherein each training computing device is configured to, based on the reference transcription having the confidence level of at least 70%, apply a weak acoustic model, the language model, and the weight vector to the of reference utterances in the respective partition to determine a set of respective secondary transcriptions, wherein the respective secondary transcriptions have respective secondary confidence levels, wherein the weak acoustic model has a higher error rate than the strong acoustic model, and wherein at least some respective secondary transcriptions are different from the respective reference transcriptions; and at least one combining computing device configured to, based on the feature vectors associated with the reference utterances of the selected partitions, update the weight vector so that transcribing the respective reference utterances using the weak acoustic model, the language model and the updated weight vector results in respective tertiary transcriptions with respective tertiary confidence levels that are greater than the respective secondary confidence levels. 19. The computing system of claim 17 , wherein each of the plurality of training computing devices applying the weak acoustic model, the language model, and the weight vector to the respective selected partitions of reference utterances produces respective weight vector adjustments.