Patent ID: 11934935
Assignee: DEEPMIND TECHNOLOGIES LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer-implemented method of training a feedforward generative neural network for generating an audio speech waveform based on an input text segment,
wherein the feedforward generative neural network comprises a plurality of feedforward parameters and is configured to receive a feedforward input comprising features of the input text segment and to process the feedforward input to generate, by using a single forward pass of the feedforward generative neural network, a feedforward output that defines an output example of the audio speech waveform, wherein the output example comprises a plurality of output samples that include, for each of a plurality of generation time steps, a respective output sample at the respective generation time step, and
wherein the training comprises:
obtaining a training context input specifying features of a text segment;
processing a training feedforward input comprising the training context input using a single forward pass of the feedforward generative neural network in accordance with current values of the feedforward parameters to generate a training feedforward output comprising output values for the plurality of generation time steps;
processing the training context input using an autoregressive generative neural network that has been trained to autoregressively generate, by using a plurality of forward passes of the trained autoregressive generative neural network, a plurality of autoregressive outputs, wherein the autoregressive generative neural network is a different and separate neural network from the feedforward generative neural network, and the processing comprises, for each respective generation time step of the plurality of generation time steps, using a respective one of the plurality of forward passes of the autoregressive generative neural network to generate a respective autoregressive output that defines a likelihood distribution over possible values at the respective generation time step conditioned on (i) the training context input and (ii) output samples defined by the training feedforward output that has been generated using a single forward pass of the feedforward generative neural network at one or more generation time steps that precede the respective generation time step;
determining a first gradient with respect to the feedforward parameters to minimize a divergence loss that depends on a plurality of first divergences that include, for each of the plurality of generation time steps, a respective first divergence from the likelihood distribution defined by the autoregressive output generated by the respective one of the plurality of forward passes of the autoregressive generative neural network for the generation time step and the likelihood distribution for the generation time step defined by the training feedforward output generated by the single forward pass of the feedforward generative neural network; and
determining an update to the current values of parameters of the feedforward generative neural network based at least in part on the first gradient.