A computer-implemented audio processing method, comprising: training a speech synthesizer, wherein the training involves: (a) receiving, by a content extraction process implemented via a control system comprising one or more processors and one or more non-transitory storage media, first audio data corresponding to first speech of a first person; (b) producing, by the content extraction process, a first time-stamped phoneme sequence and first pitch contour data corresponding to the first speech; (c) receiving, by a first neural network implemented via the control system, the first time-stamped phoneme sequence and the first pitch contour data; (d) producing, by the first neural network, first neural network output corresponding to the first time-stamped phoneme sequence and the first pitch contour data, the first neural network output comprising a plurality of frame sizes; (e) receiving, by a second neural network implemented via the control system, the first neural network output, the second neural network comprising a hierarchy of modules, each module operating at a different temporal resolution, wherein the first neural network has produced the first neural network output such that each of the plurality of frame sizes of the first neural network output corresponds to a temporal resolution of a module of the second neural network; (f) generating, by the second neural network, first predicted audio signals; (g) comparing, via the control system, the first predicted audio signals to first test data, the test data being audio data corresponding to speech of the first person; (h) determining, via the control system, a loss function value for the first predicted audio signals; and (i) repeating (a) through (h) until a difference between a current loss function value for the first predicted audio signals and a prior loss function value for the first predicted audio signals is less than or equal to a predetermined value, wherein repeating (i) comprises changing a physical state of at least one non-transitory storage medium location corresponding with at least one weight of the second neural network. (a) receiving, by a content extraction process implemented via a control system comprising one or more processors and one or more non-transitory storage media, first audio data corresponding to first speech of a first person; (b) producing, by the content extraction process, a first time-stamped phoneme sequence and first pitch contour data corresponding to the first speech; (c) receiving, by a first neural network implemented via the control system, the first time-stamped phoneme sequence and the first pitch contour data; (d) producing, by the first neural network, first neural network output corresponding to the first time-stamped phoneme sequence and the first pitch contour data, the first neural network output comprising a plurality of frame sizes; (e) receiving, by a second neural network implemented via the control system, the first neural network output, the second neural network comprising a hierarchy of modules, each module operating at a different temporal resolution, wherein the first neural network has produced the first neural network output such that each of the plurality of frame sizes of the first neural network output corresponds to a temporal resolution of a module of the second neural network; (f) generating, by the second neural network, first predicted audio signals; (g) comparing, via the control system, the first predicted audio signals to first test data, the test data being audio data corresponding to speech of the first person; (h) determining, via the control system, a loss function value for the first predicted audio signals; and (i) repeating (a) through (h) until a difference between a current loss function value for the first predicted audio signals and a prior loss function value for the first predicted audio signals is less than or equal to a predetermined value, wherein repeating (i) comprises changing a physical state of at least one non-transitory storage medium location corresponding with at least one weight of the second neural network.