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公开(公告)号：US20190385619A1
公开(公告)日：2019-12-19
申请号：US16557390
申请日：2019-08-30
Applicant: Google LLC
Inventor： Ignacio Lopez Moreno , Li Wan , Quan Wang
Abstract: Methods, systems, apparatus, including computer programs encoded on computer storage medium, to facilitate language independent-speaker verification. In one aspect, a method includes actions of receiving, by a user device, audio data representing an utterance of a user. Other actions may include providing, to a neural network stored on the user device, input data derived from the audio data and a language identifier. The neural network may be trained using speech data representing speech in different languages or dialects. The method may include additional actions of generating, based on output of the neural network, a speaker representation and determining, based on the speaker representation and a second representation, that the utterance is an utterance of the user. The method may provide the user with access to the user device based on determining that the utterance is an utterance of the user.
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公开(公告)号：US10325602B2
公开(公告)日：2019-06-18
申请号：US15666806
申请日：2017-08-02
Applicant: Google LLC
Inventor： Hasim Sak , Ignacio Lopez Moreno , Alan Sean Papir , Li Wan , Quan Wang
Abstract: Systems, methods, devices, and other techniques for training and using a speaker verification neural network. A computing device may receive data that characterizes a first utterance. The computing device provides the data that characterizes the utterance to a speaker verification neural network. Subsequently, the computing device obtains, from the speaker verification neural network, a speaker representation that indicates speaking characteristics of a speaker of the first utterance. The computing device determines whether the first utterance is classified as an utterance of a registered user of the computing device. In response to determining that the first utterance is classified as an utterance of the registered user of the computing device, the device may perform an action for the registered user of the computing device.
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公开(公告)号：US20240203400A1
公开(公告)日：2024-06-20
申请号：US18394632
申请日：2023-12-22
Applicant: GOOGLE LLC
Inventor： Ignacio Lopez Moreno , Quan Wang , Jason Pelecanos , Li Wan , Alexander Gruenstein , Hakan Erdogan
CPC classification number: G10L15/063 , G10L15/07 , G10L15/20 , G10L17/04 , G10L17/20 , G10L21/0208 , G10L2015/088
Abstract: Implementations relate to an automated assistant that can bypass invocation phrase detection when an estimation of device-to-device distance satisfies a distance threshold. The estimation of distance can be performed for a set of devices, such as a computerized watch and a cellular phone, and/or any other combination of devices. The devices can communicate ultrasonic signals between each other, and the estimated distance can be determined based on when the ultrasonic signals are sent and/or received by each respective device. When an estimated distance satisfies the distance threshold, the automated assistant can operate as if the user is holding onto their cellular phone while wearing their computerized watch. This scenario can indicate that the user may be intending to hold their device to interact with the automated assistant and, based on this indication, the automated assistant can temporarily bypass invocation phrase detection (e.g., invoke the automated assistant).
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公开(公告)号：US11854533B2
公开(公告)日：2023-12-26
申请号：US17587424
申请日：2022-01-28
Applicant: GOOGLE LLC
Inventor： Ignacio Lopez Moreno , Quan Wang , Jason Pelecanos , Li Wan , Alexander Gruenstein , Hakan Erdogan
IPC: G10L15/16 , G10L15/06 , G10L15/07 , G10L15/20 , G10L17/04 , G10L17/20 , G10L21/0208 , G10L15/08
CPC classification number: G10L15/063 , G10L15/07 , G10L15/20 , G10L17/04 , G10L17/20 , G10L21/0208 , G10L2015/088
Abstract: Techniques disclosed herein enable training and/or utilizing speaker dependent (SD) speech models which are personalizable to any user of a client device. Various implementations include personalizing a SD speech model for a target user by processing, using the SD speech model, a speaker embedding corresponding to the target user along with an instance of audio data. The SD speech model can be personalized for an additional target user by processing, using the SD speech model, an additional speaker embedding, corresponding to the additional target user, along with another instance of audio data. Additional or alternative implementations include training the SD speech model based on a speaker independent speech model using teacher student learning.
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公开(公告)号：US11646011B2
公开(公告)日：2023-05-09
申请号：US17846287
申请日：2022-06-22
Applicant: Google LLC
Inventor： Li Wan , Yang Yu , Prashant Sridhar , Ignacio Lopez Moreno , Quan Wang
IPC: G10L15/00
CPC classification number: G10L15/005
Abstract: Methods and systems for training and/or using a language selection model for use in determining a particular language of a spoken utterance captured in audio data. Features of the audio data can be processed using the trained language selection model to generate a predicted probability for each of N different languages, and a particular language selected based on the generated probabilities. Speech recognition results for the particular language can be utilized responsive to selecting the particular language of the spoken utterance. Many implementations are directed to training the language selection model utilizing tuple losses in lieu of traditional cross-entropy losses. Training the language selection model utilizing the tuple losses can result in more efficient training and/or can result in a more accurate and/or robust model—thereby mitigating erroneous language selections for spoken utterances.
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Patent Agency Ranking
公开(公告)号：US11594230B2
公开(公告)日：2023-02-28
申请号：US17307704
申请日：2021-05-04
Applicant: Google LLC
Inventor： Ignacio Lopez Moreno , Li Wan , Quan Wang
Abstract: Methods, systems, apparatus, including computer programs encoded on computer storage medium, to facilitate language independent-speaker verification. In one aspect, a method includes actions of receiving, by a user device, audio data representing an utterance of a user. Other actions may include providing, to a neural network stored on the user device, input data derived from the audio data and a language identifier. The neural network may be trained using speech data representing speech in different languages or dialects. The method may include additional actions of generating, based on output of the neural network, a speaker representation and determining, based on the speaker representation and a second representation, that the utterance is an utterance of the user. The method may provide the user with access to the user device based on determining that the utterance is an utterance of the user.
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公开(公告)号：US11545157B2
公开(公告)日：2023-01-03
申请号：US16617219
申请日：2019-04-15
Applicant: Google LLC
Inventor： Quan Wang , Yash Sheth , Ignacio Lopez Moreno , Li Wan
Abstract: Techniques are described for training and/or utilizing an end-to-end speaker diarization model. In various implementations, the model is a recurrent neural network (RNN) model, such as an RNN model that includes at least one memory layer, such as a long short-term memory (LSTM) layer. Audio features of audio data can be applied as input to an end-to-end speaker diarization model trained according to implementations disclosed herein, and the model utilized to process the audio features to generate, as direct output over the model, speaker diarization results. Further, the end-to-end speaker diarization model can be a sequence-to-sequence model, where the sequence can have variable length. Accordingly, the model can be utilized to generate speaker diarization results for any of various length audio segments.
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公开(公告)号：US20220157298A1
公开(公告)日：2022-05-19
申请号：US17587424
申请日：2022-01-28
Applicant: GOOGLE LLC
Inventor： Ignacio Lopez Moreno , Quan Wang , Jason Pelecanos , Li Wan , Alexander Gruenstein , Hakan Erdogan
Abstract: Techniques disclosed herein enable training and/or utilizing speaker dependent (SD) speech models which are personalizable to any user of a client device. Various implementations include personalizing a SD speech model for a target user by processing, using the SD speech model, a speaker embedding corresponding to the target user along with an instance of audio data. The SD speech model can be personalized for an additional target user by processing, using the SD speech model, an additional speaker embedding, corresponding to the additional target user, along with another instance of audio data. Additional or alternative implementations include training the SD speech model based on a speaker independent speech model using teacher student learning.
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公开(公告)号：US20210312907A1
公开(公告)日：2021-10-07
申请号：US17251163
申请日：2019-12-04
Applicant: GOOGLE LLC
Inventor： Ignacio Lopez Moreno , Quan Wang , Jason Pelecanos , Li Wan , Alexander Gruenstein , Hakan Erdogan
Abstract: Techniques disclosed herein enable training and/or utilizing speaker dependent (SD) speech models which are personalizable to any user of a client device. Various implementations include personalizing a SD speech model for a target user by processing, using the SD speech model, a speaker embedding corresponding to the target user along with an instance of audio data. The SD speech model can be personalized for an additional target user by processing, using the SD speech model, an additional speaker embedding, corresponding to the additional target user, along with another instance of audio data. Additional or alternative implementations include training the SD speech model based on a speaker independent speech model using teacher student learning.
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20.发明申请公开(公告)号：US20200335083A1
公开(公告)日：2020-10-22
申请号：US16959037
申请日：2019-11-27
Applicant: Google LLC
Inventor： Li Wan , Yang Yu , Prashant Sridhar , Ignacio Lopez Moreno , Quan Wang
IPC: G10L15/00
Abstract: Methods and systems for training and/or using a language selection model for use in determining a particular language of a spoken utterance captured in audio data. Features of the audio data can be processed using the trained language selection model to generate a predicted probability for each of N different languages, and a particular language selected based on the generated probabilities. Speech recognition results for the particular language can be utilized responsive to selecting the particular language of the spoken utterance. Many implementations are directed to training the language selection model utilizing tuple losses in lieu of traditional cross-entropy losses. Training the language selection model utilizing the tuple losses can result in more efficient training and/or can result in a more accurate and/or robust model—thereby mitigating erroneous language selections for spoken utterances.
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