13. A computing device, comprising: a processor; and a computer-readable storage medium having stored thereon program instructions that, upon execution by the processor, cause the computing device to perform operations comprising: receiving training samples for training a neural network to learn an acoustic speech model, wherein at least one training sample of the training samples represents at least one phone of captured speech, determining a curriculum function for acoustic speech modeling, wherein the curriculum function assigns a difficulty value for a designated training sample of the training samples based on a combination comprising a duration value for the designated training sample and a sound quality value for the designated training sample, for each training sample of the training samples, determining a corresponding difficulty value for the training sample using the curriculum function, ordering the training samples based on the corresponding difficulty values for the training samples, presenting the ordered training samples to train the neural network on at least a portion of the acoustic speech model, and recognizing a received speech sample using the trained neural network.
13. A computing device, comprising: a processor; and a computer-readable storage medium having stored thereon program instructions that, upon execution by the processor, cause the computing device to perform operations comprising: receiving training samples for training a neural network to learn an acoustic speech model, wherein at least one training sample of the training samples represents at least one phone of captured speech, determining a curriculum function for acoustic speech modeling, wherein the curriculum function assigns a difficulty value for a designated training sample of the training samples based on a combination comprising a duration value for the designated training sample and a sound quality value for the designated training sample, for each training sample of the training samples, determining a corresponding difficulty value for the training sample using the curriculum function, ordering the training samples based on the corresponding difficulty values for the training samples, presenting the ordered training samples to train the neural network on at least a portion of the acoustic speech model, and recognizing a received speech sample using the trained neural network. 15. The computing device of claim 13 , wherein a sample corresponding to a lower difficulty value is less difficult for the neural network to process than a sample corresponding to a higher difficulty value.