Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying a user in a multi-user environment. One of the methods includes receiving, by a first user device, an audio signal encoding an utterance, obtaining, by the first user device, a first speaker model for a first user of the first user device, obtaining, by the first user device for a second user of a second user device that is co-located with the first user device, a second speaker model for the second user or a second score that indicates a respective likelihood that the utterance was spoken by the second user, and determining, by the first user device, that the utterance was spoken by the first user using (i) the first speaker model and the second speaker model or (ii) the first speaker model and the second score.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying a user in a multi-user environment. One of the methods includes receiving, by a first user device, an audio signal encoding an utterance, obtaining, by the first user device, a first speaker model for a first user of the first user device, obtaining, by the first user device for a second user of a second user device that is co-located with the first user device, a second speaker model for the second user or a second score that indicates a respective likelihood that the utterance was spoken by the second user, and determining, by the first user device, that the utterance was spoken by the first user using (i) the first speaker model and the second speaker model or (ii) the first speaker model and the second score.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speaker verification. The methods, systems, and apparatus include actions of inputting speech data that corresponds to a particular utterance to a first neural network and determining an evaluation vector based on output at a hidden layer of the first neural network. Additional actions include obtaining a reference vector that corresponds to a past utterance of a particular speaker. Further actions include inputting the evaluation vector and the reference vector to a second neural network that is trained on a set of labeled pairs of feature vectors to identify whether speakers associated with the labeled pairs of feature vectors are the same speaker. More actions include determining, based on an output of the second neural network, whether the particular utterance was likely spoken by the particular speaker.