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    Neural networks for speaker verification
    23.
    发明授权
    Neural networks for speaker verification 有权

    公开(公告)号：US12148433B2

    公开(公告)日：2024-11-19

    申请号：US18485069

    申请日：2023-10-11

    Applicant: Google LLC

    Inventor： Georg Heigold ,  Samuel Bengio ,  Ignacio Lopez Moreno

    IPC: G10L17/18 ,  G10L17/02 ,  G10L17/04

    Abstract: This document generally describes systems, methods, devices, and other techniques related to speaker verification, including (i) training a neural network for a speaker verification model, (ii) enrolling users at a client device, and (iii) verifying identities of users based on characteristics of the users' voices. Some implementations include a computer-implemented method. The method can include receiving, at a computing device, data that characterizes an utterance of a user of the computing device. A speaker representation can be generated, at the computing device, for the utterance using a neural network on the computing device. The neural network can be trained based on a plurality of training samples that each: (i) include data that characterizes a first utterance and data that characterizes one or more second utterances, and (ii) are labeled as a matching speakers sample or a non-matching speakers sample.

    Neural Networks For Speaker Verification
    24.
    发明申请
    Neural Networks For Speaker Verification 有权

    公开(公告)号：US20210366491A1

    公开(公告)日：2021-11-25

    申请号：US17444384

    申请日：2021-08-03

    Applicant: Google LLC

    Inventor： Georg Heigold ,  Samuel Bengio ,  Ignacio Lopez Moreno

    IPC: G10L17/18 ,  G10L17/04 ,  G10L17/02

    Abstract: This document generally describes systems, methods, devices, and other techniques related to speaker verification, including (i) training a neural network for a speaker verification model, (ii) enrolling users at a client device, and (iii) verifying identities of users based on characteristics of the users' voices. Some implementations include a computer-implemented method. The method can include receiving, at a computing device, data that characterizes an utterance of a user of the computing device. A speaker representation can be generated, at the computing device, for the utterance using a neural network on the computing device. The neural network can be trained based on a plurality of training samples that each: (i) include data that characterizes a first utterance and data that characterizes one or more second utterances, and (ii) are labeled as a matching speakers sample or a non-matching speakers sample.

    Device placement optimization with reinforcement learning
    26.
    发明授权
    Device placement optimization with reinforcement learning 有权

    公开(公告)号：US10692003B2

    公开(公告)日：2020-06-23

    申请号：US16445330

    申请日：2019-06-19

    Applicant: Google LLC

    Inventor： Samuel Bengio ,  Mohammad Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

    IPC: G06N3/08 ,  G06N5/04 ,  G06N3/04 ,  G06N3/10

    Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

    Neural Networks For Speaker Verification
    27.
    发明申请
    Neural Networks For Speaker Verification 审中-公开

    公开(公告)号：US20200160869A1

    公开(公告)日：2020-05-21

    申请号：US16752007

    申请日：2020-01-24

    Applicant: Google LLC

    Inventor： Georg Heigold ,  Samuel Bengio ,  Ignacio Lopez Moreno

    IPC: G10L17/18 ,  G10L17/02 ,  G10L17/04

    Abstract: This document generally describes systems, methods, devices, and other techniques related to speaker verification, including (i) training a neural network for a speaker verification model, (ii) enrolling users at a client device, and (iii) verifying identities of users based on characteristics of the users' voices. Some implementations include a computer-implemented method. The method can include receiving, at a computing device, data that characterizes an utterance of a user of the computing device. A speaker representation can be generated, at the computing device, for the utterance using a neural network on the computing device. The neural network can be trained based on a plurality of training samples that each: (i) include data that characterizes a first utterance and data that characterizes one or more second utterances, and (ii) are labeled as a matching speakers sample or a non-matching speakers sample.

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