1. A method, comprising: receiving, at a computing device, 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 the neural network using the computing device 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.
1. A method, comprising: receiving, at a computing device, 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 the neural network using the computing device 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. 5. The method of claim 1 , wherein the combination further comprises an estimate from a posteriori estimator function.