Abstract: Systems, apparatuses, and methods directed to training a Generator that is part of a Generative Adversarial Network (GAN) to generate “realistic” examples of a distribution. In some embodiments, this includes training the Generator model to receive a tensor as an input and generate a distribution which is then converted to the frequency domain and used to determine a loss term. Similarly, an actual distribution is also converted to the frequency domain and used to determine a loss term. The loss terms, generated distribution, and actual distribution are provided to a Discriminator which generates loss terms used as part of a backpropagation or feedback mechanism to modify the operation of the Generator and/or Discriminator.

Abstract: Systems, apparatuses, and methods directed to generating waveforms that can be used to produce realistic sounding speech when used in a speech synthesis or text-to-speech application. In some embodiments, this includes training a neural network model that is part of a generative adversarial network (GAN) to generate the waveform from an input spectrogram, while implementing a specific set of processing stages to determine loss. The loss is used with a back-propagation process to update the weights of the model during a training cycle. In one example use case, text is converted to a spectrogram and the spectrogram is converted to a waveform by a trained model. The output of the model may be used to drive a transducer that converts the waveform to audible sound.