1. A method for asynchronously training a plurality of neural networks of sequence-training speech models, comprising: performing a first training process comprising: obtaining, for a first sequence-training speech model, a first batch of training frames that represent speech features of first training utterances; obtaining, for the first sequence-training speech model, one or more first neural network parameters; and determining, for the first sequence-training speech model, one or more adjusted first neural network parameters based on (i) the first batch of training frames and (ii) the one or more first neural network parameters; performing a second training process comprising: obtaining, for a second sequence-training speech model, a second batch of training frames that represent speech features of second training utterances, wherein the obtaining of the second batch of training frames by the second sequence-training speech model is independent of the obtaining of the first batch of training frames by the first sequence-training speech model; obtaining, for the second sequence-training speech model, one or more second neural network parameters, wherein the obtaining of the second neural network parameters by the second sequence-training speech model is independent of (i) the obtaining of the first neural network parameters by the first sequence-training speech model and (ii) the determining of the one or more adjusted first neural network parameters by the first sequence-training speech model; and determining, for the second sequence-training speech model, one or more adjusted second neural network parameters based on (i) the second batch of training frames and (ii) the one or more second neural network parameters, wherein the determining of the one or more adjusted second neural network parameters for the second sequence-training speech model is independent of the determining of the one or more adjusted first neural network parameters for the first sequence-training speech model; providing the one or more adjusted first neural network parameters and the one or more adjusted second neural network parameters to a computing system; and determining, by the computing system, neural network parameters for a third sequence-training speech model based on the one or more adjusted first neural network parameters and the one or more adjusted second neural network parameters; wherein the first training process is performed concurrently with the second training process and asynchronously with respect to the second training process.
2. The method of claim 1 , wherein obtaining the first batch of training frames that represent speech features of first training utterances comprises: obtaining, by a first decoder associated with the first sequence-training speech model, an utterance of the first training utterances; determining, by the first decoder, a reference score associated with the utterance; determining, by the first decoder, a training frame representing acoustic characteristics of the utterance based on the reference score.