公开(公告)号：US12027160B2

公开(公告)日：2024-07-02

申请号：US18074691

申请日：2022-12-05

Applicant: GOOGLE LLC

Inventor： Aleks Kracun ,  Niranjan Subrahmanya ,  Aishanee Shah

IPC: G10L15/22 ,  G10L15/06 ,  G10L15/197 ,  G10L15/08

CPC classification number: G10L15/197 ,  G10L15/063 ,  G10L15/22 ,  G10L2015/088 ,  G10L2015/223

Abstract: Techniques are described herein for improving performance of machine learning model(s) and thresholds utilized in determining whether automated assistant function(s) are to be initiated. A method includes: receiving, via one or more microphones of a client device, audio data that captures a spoken utterance of a user; processing the audio data using a machine learning model to generate a predicted output that indicates a probability of one or more hotwords being present in the audio data; determining that the predicted output satisfies a secondary threshold that is less indicative of the one or more hotwords being present in the audio data than is a primary threshold; in response to determining that the predicted output satisfies the secondary threshold, prompting the user to indicate whether or not the spoken utterance includes a hotword; receiving, from the user, a response to the prompting; and adjusting the primary threshold based on the response.

公开(公告)号：US20230101572A1

公开(公告)日：2023-03-30

申请号：US18074691

申请日：2022-12-05

Applicant: GOOGLE LLC

Inventor： Aleks Kracun ,  Niranjan Subrahmanya ,  Aishanee Shah

IPC: G10L15/197 ,  G10L15/06 ,  G10L15/22

Abstract: Techniques are described herein for improving performance of machine learning model(s) and thresholds utilized in determining whether automated assistant function(s) are to be initiated. A method includes: receiving, via one or more microphones of a client device, audio data that captures a spoken utterance of a user; processing the audio data using a machine learning model to generate a predicted output that indicates a probability of one or more hotwords being present in the audio data; determining that the predicted output satisfies a secondary threshold that is less indicative of the one or more hotwords being present in the audio data than is a primary threshold; in response to determining that the predicted output satisfies the secondary threshold, prompting the user to indicate whether or not the spoken utterance includes a hotword; receiving, from the user, a response to the prompting; and adjusting the primary threshold based on the response.

公开(公告)号：US20210390948A1

公开(公告)日：2021-12-16

申请号：US16898278

申请日：2020-06-10

Applicant: Google LLC

Inventor： Aishanee Shah ,  Alexander H. Gruenstein ,  Ian C. Mcgraw

IPC: G10L15/065 ,  G10L15/22

Abstract: A method for automatic hotword threshold tuning includes receiving, from a user device executing a first stage hotword detector configured to detect a hotword in streaming audio, audio data characterizing the detected hotword. The method includes processing, using a second stage hotword detector, the audio data to determine whether the hotword is detected by the second stage hotword detector. When the hotword is not detected, the method includes identifying a false acceptance instance at the first stage hotword detector indicating that the first stage hotword detector incorrectly detected the hotword. The method includes determining whether a false acceptance rate satisfies a false acceptance rate threshold based on a number of false acceptance instances within a false acceptance time period. When the false acceptance rate satisfies the false acceptance rate threshold, the method includes adjusting the hotword detection threshold of the first stage hotword detector.

公开(公告)号：US20240355324A1

公开(公告)日：2024-10-24

申请号：US18761117

申请日：2024-07-01

Applicant: GOOGLE LLC

Inventor： Aleks Kracun ,  Niranjan Subrahmanya ,  Aishanee Shah

IPC: G10L15/197 ,  G10L15/06 ,  G10L15/08 ,  G10L15/22

CPC classification number: G10L15/197 ,  G10L15/063 ,  G10L15/22 ,  G10L2015/088 ,  G10L2015/223

Abstract: Techniques are described herein for improving performance of machine learning model(s) and thresholds utilized in determining whether automated assistant function(s) are to be initiated. A method includes: receiving, via one or more microphones of a client device, audio data that captures a spoken utterance of a user; processing the audio data using a machine learning model to generate a predicted output that indicates a probability of one or more hotwords being present in the audio data; determining that the predicted output satisfies a secondary threshold that is less indicative of the one or more hotwords being present in the audio data than is a primary threshold; in response to determining that the predicted output satisfies the secondary threshold, prompting the user to indicate whether or not the spoken utterance includes a hotword; receiving, from the user, a response to the prompting; and adjusting the primary threshold based on the response.

公开(公告)号：US11610578B2

公开(公告)日：2023-03-21

申请号：US16898278

申请日：2020-06-10

Applicant: Google LLC

Inventor： Aishanee Shah ,  Alexander H. Gruenstein ,  Ian C. Mcgraw

IPC: G10L15/065 ,  G10L15/22

Abstract: A method for automatic hotword threshold tuning includes receiving, from a user device executing a first stage hotword detector configured to detect a hotword in streaming audio, audio data characterizing the detected hotword. The method includes processing, using a second stage hotword detector, the audio data to determine whether the hotword is detected by the second stage hotword detector. When the hotword is not detected, the method includes identifying a false acceptance instance at the first stage hotword detector indicating that the first stage hotword detector incorrectly detected the hotword. The method includes determining whether a false acceptance rate satisfies a false acceptance rate threshold based on a number of false acceptance instances within a false acceptance time period. When the false acceptance rate satisfies the false acceptance rate threshold, the method includes adjusting the hotword detection threshold of the first stage hotword detector.

公开(公告)号：US20220068268A1

公开(公告)日：2022-03-03

申请号：US17011612

申请日：2020-09-03

Applicant: Google LLC

Inventor： Aleks Kracun ,  Niranjan Subrahmanya ,  Aishanee Shah

IPC: G10L15/197 ,  G10L15/06 ,  G10L15/22

Abstract: Techniques are described herein for improving performance of machine learning model(s) and thresholds utilized in determining whether automated assistant function(s) are to be initiated. A method includes: receiving, via one or more microphones of a client device, audio data that captures a spoken utterance of a user; processing the audio data using a machine learning model to generate a predicted output that indicates a probability of one or more hotwords being present in the audio data; determining that the predicted output satisfies a secondary threshold that is less indicative of the one or more hotwords being present in the audio data than is a primary threshold; in response to determining that the predicted output satisfies the secondary threshold, prompting the user to indicate whether or not the spoken utterance includes a hotword; receiving, from the user, a response to the prompting; and adjusting the primary threshold based on the response.

公开(公告)号：US12277928B2

公开(公告)日：2025-04-15

申请号：US18181895

申请日：2023-03-10

Applicant: Google LLC

Inventor： Aishanee Shah ,  Alexander H. Gruenstein ,  Ian C. McGraw

IPC: G10L15/065 ,  G10L15/22

Abstract: A method for automatic hotword threshold tuning includes receiving, from a user device executing a first stage hotword detector configured to detect a hotword in streaming audio, audio data characterizing the detected hotword. The method includes processing, using a second stage hotword detector, the audio data to determine whether the hotword is detected by the second stage hotword detector. When the hotword is not detected, the method includes identifying a false acceptance instance at the first stage hotword detector indicating that the first stage hotword detector incorrectly detected the hotword. The method includes determining whether a false acceptance rate satisfies a false acceptance rate threshold based on a number of false acceptance instances within a false acceptance time period. When the false acceptance rate satisfies the false acceptance rate threshold, the method includes adjusting the hotword detection threshold of the first stage hotword detector.

公开(公告)号：US20230206908A1

公开(公告)日：2023-06-29

申请号：US18181895

申请日：2023-03-10

Applicant: Google LLC

Inventor： Aishanee Shah ,  Alexander H. Gruenstein ,  Ian C. McGraw

IPC: G10L15/065 ,  G10L15/22

CPC classification number: G10L15/065 ,  G10L15/22 ,  G10L2015/223

Abstract: A method for automatic hotword threshold tuning includes receiving, from a user device executing a first stage hotword detector configured to detect a hotword in streaming audio, audio data characterizing the detected hotword. The method includes processing, using a second stage hotword detector, the audio data to determine whether the hotword is detected by the second stage hotword detector. When the hotword is not detected, the method includes identifying a false acceptance instance at the first stage hotword detector indicating that the first stage hotword detector incorrectly detected the hotword. The method includes determining whether a false acceptance rate satisfies a false acceptance rate threshold based on a number of false acceptance instances within a false acceptance time period. When the false acceptance rate satisfies the false acceptance rate threshold, the method includes adjusting the hotword detection threshold of the first stage hotword detector.

公开(公告)号：US11521604B2

公开(公告)日：2022-12-06

申请号：US17011612

申请日：2020-09-03

Applicant: Google LLC

Inventor： Aleks Kracun ,  Niranjan Subrahmanya ,  Aishanee Shah

IPC: G10L15/22 ,  G10L15/197 ,  G10L15/06 ,  G10L15/08

Abstract: Techniques are described herein for improving performance of machine learning model(s) and thresholds utilized in determining whether automated assistant function(s) are to be initiated. A method includes: receiving, via one or more microphones of a client device, audio data that captures a spoken utterance of a user; processing the audio data using a machine learning model to generate a predicted output that indicates a probability of one or more hotwords being present in the audio data; determining that the predicted output satisfies a secondary threshold that is less indicative of the one or more hotwords being present in the audio data than is a primary threshold; in response to determining that the predicted output satisfies the secondary threshold, prompting the user to indicate whether or not the spoken utterance includes a hotword; receiving, from the user, a response to the prompting; and adjusting the primary threshold based on the response.