Encoder-decoder models for sequence to sequence mapping

A non-transitory computer-readable storage medium comprising instructions stored thereon that are executable by a processing device and upon such execution cause the processing device to train a recurrent neural network to process received input acoustic sequences and generate sequences of outputs, the generated sequence of outputs indicating output labels from among a predetermined set of output labels including output labels that respectively correspond to different linguistic units and to a blank label that does not represent a classification of a received input acoustic sequence, the training comprising:

• obtaining a plurality of training examples, each training example comprising (i) an input acoustic sequence of scores indicative of acoustic data at each of multiple time steps in a series of time steps, the input acoustic sequence representing a known utterance, and (ii) a corresponding target sequence of linguistic units representing a transcription of the known utterance;
• and
• training the recurrent neural network to minimize a negative log likelihood loss function using the plurality of training examples by
• representing possible alignments between the input acoustic sequence and the target sequence of linguistic units as a lattice, the possible alignments constrained to allow placeholder label repetitions only and each node in the lattice represents a respective state of the recurrent neural network, each state of the recurrent neural network being dependent on a respective time step from the series of time steps and a respective position in the target sequence of linguistic units, and wherein transitions between nodes in the lattice represent probabilities of observing respective subsequent linguistic units or placeholder labels in the target sequence of linguistic units,
• performing forward calculations through the lattice to update each recurrent neural network state,
• approximating, the log likelihood loss function using the updated recurrent neural network states, and
• performing back propagation techniques using the approximated log likelihood function to adjust recurrent neural network parameters to trained recurrent neural network parameters.