Deepmind Technologies Limited

Ioannis Alexandros Assael

Brendan Shillingford

Joao Ferdinando Gomes De Freitas

Oriol Vinyals

Aaron Gerard Antonius Van Den Oord

Scott Ellison Reed

Yutian Chen

Heiga Zen

Jakob Nicolaus Foerster

Thomas Chadwick Walters

Archit Gupta

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing video data using an adaptive visual speech recognition model. One of the methods includes receiving a video that includes a plurality of video frames that depict a first speaker: obtaining a first embedding characterizing the first speaker; and processing a first input comprising (i) the video and (ii) the first embedding using a visual speech recognition neural network having a plurality of parameters, wherein the visual speech recognition neural network is configured to process the video and the first embedding in accordance with trained values of the parameters to generate a speech recognition output that defines a sequence of one or more words being spoken by the first speaker in the video.

Adaptive visual speech recognition

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bandwidth extension. One of the methods includes obtaining a low-resolution version of an input, the low-resolution version of the input comprising a first number of samples at a first sample rate over a first time period; and generating, from the low-resolution version of the input, a high-resolution version of the input comprising a second, larger number of samples at a second, higher sample rate over the first time period. Generating the high-resolution version includes generating a representation of the low-resolution version of the input; processing the representation of the low-resolution version of the input through a conditioning neural network to generate a conditioning input; and processing the conditioning input using a generative neural network to generate the high/resolution version of the input.

Bandwidth extension of incoming data using neural networks

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating audio output samples predicted to be communicated by a user. One example system includes a first user device having a first user. The first user device initiates a communication session between the first user and a second user of a second user device. The first user device obtains a neural network model of the second user. The neural network model is trained to generate, conditioned on audio input samples received up to a current time step, an audio output sample predicted to be communicated by the second user at a next time step. The user device repeatedly provides received audio input samples as input to the neural network model and plays audio output samples generated by the neural network model in place of received audio input samples communicated by the second user.

Resolving time-delays using generative models

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing visual speech recognition. In one aspect, a method comprises receiving a video comprising a plurality of video frames, wherein each video frame depicts a pair of lips; processing the video using a visual speech recognition neural network to generate, for each output position in an output sequence, a respective output score for each token in a vocabulary of possible tokens, wherein the visual speech recognition neural network comprises one or more volumetric convolutional neural network layers and one or more time-aggregation neural network layers; wherein the vocabulary of possible tokens comprises a plurality of phonemes; and determining a sequence of words expressed by the pair of lips depicted in the video using the output scores.

Visual speech recognition by phoneme prediction

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an adaptive audio-generation model. One of the methods includes generating an adaptive audio-generation model including learning a plurality of embedding vectors and parameter values of a neural network using training data comprising first text and audio data representing a plurality of different individual speakers speaking portions of the first text, wherein the plurality of embedding vectors represent respective voice characteristics of the plurality of different individual speakers. The adaptive audio-generation model is adapted for a new individual speaker using adaptation data comprising second text and audio data representing the new individual speaker speaking portions of the second text, the new individual speaker being different from each of the plurality of individual speakers, wherein adapting the audio-generation model includes learning a new embedding vector for the new individual speaker.

Sample-efficient adaptive text-to-speech

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a video speech recognition model having a plurality of model parameters on a set of unlabeled video-audio data and using a trained speech recognition model. During the training, the values of the parameters of the trained audio speech recognition model fixed are generally fixed and only the values of the video speech recognition model are adjusted. Once being trained, the video speech recognition model can be used to recognize speech from video when corresponding audio is not available.

Growing community of inventors

London, United Kingdom

Ioannis Alexandros Assael