Global neural transducer models leveraging sub-task networks

Abstract

A computer-implemented method for training a neural transducer for speech recognition is provided. The method includes initializing the neural transducer having a prediction network and an encoder network and a joint network. The method further includes expanding the prediction network by changing the prediction network to a plurality of prediction-net branches. Each of the prediction-net branches is a prediction network for a respective specific sub-task from among a plurality of specific sub-tasks. The method also includes training, by a hardware processor, an entirety of the neural transducer by using training data sets for all of the plurality of specific sub-tasks. The method additionally includes obtaining a trained neural transducer by fusing the plurality of prediction-net branches.