Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to perform speech synthesis. One of the methods includes obtaining a training data set for training a first neural network to process a spectral representation of an audio sample and to generate a prediction of the audio sample, wherein, after training, the first neural network obtains spectral representations of audio samples from a second neural network; for a plurality of audio samples in the training data set: generating a ground-truth spectral representation of the audio sample; and processing the ground-truth spectral representation using a third neural network to generate an updated spectral representation of the audio sample; and training the first neural network using the updated spectral representations, wherein the third neural network is configured to generate updated spectral representations that resemble spectral representations generated by the second neural network.

Abstract: A method includes obtaining training data including a plurality of training audio signals and corresponding transcripts. Each training audio signal is spoken by a target speaker in a first accent/dialect. For each training audio signal of the training data, the method includes generating a training synthesized speech representation spoken by the target speaker in a second accent/dialect different than the first accent/dialect and training a text-to-speech (TTS) system based on the corresponding transcript and the training synthesized speech representation. The method also includes receiving an input text utterance to be synthesized into speech in the second accent/dialect. The method also includes obtaining conditioning inputs that include a speaker embedding and an accent/dialect identifier that identifies the second accent/dialect.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to perform speech synthesis. One of the methods includes obtaining a training data set for training a first neural network to process a spectral representation of an audio sample and to generate a prediction of the audio sample, wherein, after training, the first neural network obtains spectral representations of audio samples from a second neural network; for a plurality of audio samples in the training data set: generating a ground-truth spectral representation of the audio sample; and processing the ground-truth spectral representation using a third neural network to generate an updated spectral representation of the audio sample; and training the first neural network using the updated spectral representations, wherein the third neural network is configured to generate updated spectral representations that resemble spectral representations generated by the second neural network.

Abstract: A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.

Abstract: A method of building a text-to-speech (TTS) system from a small amount of speech data includes receiving a first plurality of recorded speech samples from an assortment of speakers and a second plurality of recorded speech samples from a target speaker where the assortment of speakers does not include the target speaker. The method further includes training a TTS model using the first plurality of recorded speech samples from the assortment of speakers. Here, the trained TTS model is configured to output synthetic speech as an audible representation of a text input. The method also includes re-training the trained TTS model using the second plurality of recorded speech samples from the target speaker combined with the first plurality of recorded speech samples from the assortment of speakers. Here, the re-trained TTS model is configured to output synthetic speech resembling speaking characteristics of the target speaker.

Abstract: A method of building a text-to-speech (TTS) system from a small amount of speech data includes receiving a first plurality of recorded speech samples from an assortment of speakers and a second plurality of recorded speech samples from a target speaker where the assortment of speakers does not include the target speaker. The method further includes training a TTS model using the first plurality of recorded speech samples from the assortment of speakers. Here, the trained TTS model is configured to output synthetic speech as an audible representation of a text input. The method also includes re-training the trained TTS model using the second plurality of recorded speech samples from the target speaker combined with the first plurality of recorded speech samples from the assortment of speakers. Here, the re-trained TTS model is configured to output synthetic speech resembling speaking characteristics of the target speaker.

Abstract: A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.

Abstract: A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output audio examples using a generative neural network. One of the methods includes obtaining a training conditioning text input; processing a training generative input comprising the training conditioning text input using a feedforward generative neural network to generate a training audio output; processing the training audio output using each of a plurality of discriminators, wherein the plurality of discriminators comprises one or more conditional discriminators and one or more unconditional discriminators; determining a first combined prediction by combining the respective predictions of the plurality of discriminators; and determining an update to current values of a plurality of generative parameters of the feedforward generative neural network to increase a first error in the first combined prediction.

Abstract: A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.