Systems and methods for automatic unit selection and target decomposition for sequence labelling

A computer-implemented method for training a recurrent neural network (RNN) model for speech transcription, the method comprising:

• inputting a plurality of utterance and associated labels to the RNN model, the utterances and associated labels being sampled from a training set, the RNN model comprising one or more convolutional layers and one or more recurrent layers;
• predicting one or more units among an established gram set through the one or more recurrent layers of the RNN model;
• obtaining a probability distribution over the predicted units in an output layer of the RNN model;
• and
• implementing a Gram Connectionist Temporal Classification (Gram-CTC) loss function to measure an error in prediction of a unit for the utterance given the associated label, the Gram-CTC loss function automatically selects useful grams and decomposes the input sequence with the selected grams;
• computing a derivative of the loss with respect to parameters of the RNN model;
• and
• updating the RNN model using the derivative through back-propagation.

A non-transitory computer-readable medium or media comprising one or more sequences of instructions which, when executed by one or more microprocessors, causes the steps to be performed comprising:

• receiving a plurality of utterance sequences, each utterance sequence and associated label being sampled from a training set;
• outputting a probability distribution over predicted units corresponding to the utterance sequences to a Gram Connectionist Temporal Classification (Gram-CTC) loss function;
• the Gram-CTC loss function automatically selects useful grams among uni-grams and selected high-order grams to decomposes the utterance sequence with the selected grams;
• and
• implementing a Gram-CTC loss function algorithm to measure an error of the predicted one or more characters for each utterance given the associated labels, the Gram-CTC loss function implementation involving forward-backward transitions.