Abstract: In one embodiment, a method includes accessing a first utterance of a content by a first speaker, generating first discrete speech units from the first utterance based on a speech-learning model, wherein each of the first discrete speech units is associated with a speech cluster, accessing second utterances of the content by second speakers different from the first speaker, and training a speech normalizer by processing each of the second utterances using the speech normalizer to generate second discrete speech units and updating the speech normalizer by using the first discrete speech units as an optimization target for the second discrete speech units associated with each of the second utterances.

Abstract: A method for sequence-to-sequence prediction using a neural network model includes A method for sequence-to-sequence prediction using a neural network model, generating an encoded representation based on an input sequence using an encoder of the neural network model, predicting a fertility sequence based on the input sequence, generating an output template based on the input sequence and the fertility sequence, and predicting an output sequence based on the encoded representation and the output template using a decoder of the neural network model. The neural network model includes a plurality of model parameters learned according to a machine learning process. Each item of the fertility sequence includes a fertility count associated with a corresponding item of the input sequence.

Abstract: A method for sequence-to-sequence prediction using a neural network model includes A method for sequence-to-sequence prediction using a neural network model, generating an encoded representation based on an input sequence using an encoder of the neural network model, predicting a fertility sequence based on the input sequence, generating an output template based on the input sequence and the fertility sequence, and predicting an output sequence based on the encoded representation and the output template using a decoder of the neural network model. The neural network model includes a plurality of model parameters learned according to a machine learning process. Each item of the fertility sequence includes a fertility count associated with a corresponding item of the input sequence.