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    Computing device having a multiple battery cell architecture including a temperature sensor and cooling element
    13.
    发明授权
    Computing device having a multiple battery cell architecture including a temperature sensor and cooling element 有权

    公开(公告)号：US12255292B2

    公开(公告)日：2025-03-18

    申请号：US18606897

    申请日：2024-03-15

    Applicant: Google LLC

    Inventor： James Robert Lim ,  Wei Li ,  Brian Conner ,  Brett Wilson

    IPC: H01M10/48 ,  H01M10/42

    Abstract: An example outdoor mounted device includes a first battery configured to operate at a low temperature range that at least includes negative 20 Celsius; a second battery configured to operate at a high temperature range; a temperature sensor; and processing circuitry configured to: determine, based on data received from the temperature sensors, a current temperature; responsive to determining that the current temperature is within the low temperature range, cause one or more components of the computing device to operate using electrical energy sourced from the first battery; and responsive to determining that the current temperature is within the high temperature range, cause the one or more components of the computing device to operate using electrical energy sourced from the second battery.

    TWO-PASS END TO END SPEECH RECOGNITION
    14.
    发明申请
    TWO-PASS END TO END SPEECH RECOGNITION 有权

    公开(公告)号：US20240420687A1

    公开(公告)日：2024-12-19

    申请号：US18815537

    申请日：2024-08-26

    Applicant: GOOGLE LLC

    Inventor： Tara N. Sainath ,  Yanzhang He ,  Bo Li ,  Arun Narayanan ,  Ruoming Pang ,  Antoine Jean Bruguier ,  Shuo-yiin Chang ,  Wei Li

    IPC: G10L15/16 ,  G06N3/08 ,  G10L15/05 ,  G10L15/06 ,  G10L15/22

    Abstract: Two-pass automatic speech recognition (ASR) models can be used to perform streaming on-device ASR to generate a text representation of an utterance captured in audio data. Various implementations include a first-pass portion of the ASR model used to generate streaming candidate recognition(s) of an utterance captured in audio data. For example, the first-pass portion can include a recurrent neural network transformer (RNN-T) decoder. Various implementations include a second-pass portion of the ASR model used to revise the streaming candidate recognition(s) of the utterance and generate a text representation of the utterance. For example, the second-pass portion can include a listen attend spell (LAS) decoder. Various implementations include a shared encoder shared between the RNN-T decoder and the LAS decoder.

    Multiple Battery Cell Architecture for Outdoor Mounted Computing Devices
    15.
    发明公开
    Multiple Battery Cell Architecture for Outdoor Mounted Computing Devices 审中-公开

    公开(公告)号：US20240222714A1

    公开(公告)日：2024-07-04

    申请号：US18606897

    申请日：2024-03-15

    Applicant: Google LLC

    Inventor： James Robert Lim ,  Wei Li ,  Brian Conner ,  Brett Wilson

    IPC: H01M10/42 ,  H01M10/48

    CPC classification number: H01M10/425 ,  H01M10/482 ,  H01M10/486 ,  H01M2010/4271

    Abstract: An example outdoor mounted device includes a first battery configured to operate at a low temperature range that at least includes negative 20 Celsius; a second battery configured to operate at a high temperature range; a temperature sensor; and processing circuitry configured to: determine, based on data received from the temperature sensors, a current temperature; responsive to determining that the current temperature is within the low temperature range, cause one or more components of the computing device to operate using electrical energy sourced from the first battery; and responsive to determining that the current temperature is within the high temperature range, cause the one or more components of the computing device to operate using electrical energy sourced from the second battery.

    Dynamic training of Models
    16.
    发明公开
    Dynamic training of Models 审中-公开

    公开(公告)号：US20240029413A1

    公开(公告)日：2024-01-25

    申请号：US18350845

    申请日：2023-07-12

    Applicant: Google LLC

    Inventor： Anthony Jacob Piergiovanni ,  Weiching Kuo ,  Wei Li ,  Anelia Angelova

    IPC: G06V10/774 ,  G06V10/25

    CPC classification number: G06V10/774 ,  G06V10/25 ,  G06V2201/07

    Abstract: A method involves the training of a model by dynamically adjusting the number of examples within each training batch. The dynamic adjustment is accomplished by adjusting the number of examples per task within each training batch according to the performance of the model on the tasks that the model is being trained on. In some embodiments, this method is applied to cross-modal vision-language tasks. This model may also be applied to the pre-training of a model that can be later fine-tuned for a more specific task(s).

    Learning word-level confidence for subword end-to-end automatic speech recognition
    17.
    发明授权
    Learning word-level confidence for subword end-to-end automatic speech recognition 有权

    公开(公告)号：US11610586B2

    公开(公告)日：2023-03-21

    申请号：US17182592

    申请日：2021-02-23

    Applicant: Google LLC

    Inventor： David Qiu ,  Qiujia Li ,  Yanzhang He ,  Yu Zhang ,  Bo Li ,  Liangliang Cao ,  Rohit Prabhavalkar ,  Deepti Bhatia ,  Wei Li ,  Ke Hu ,  Tara Sainath ,  Ian Mcgraw

    IPC: G10L15/22 ,  G10L15/08 ,  G06N3/08 ,  G10L25/30

    Abstract: A method includes receiving a speech recognition result, and using a confidence estimation module (CEM), for each sub-word unit in a sequence of hypothesized sub-word units for the speech recognition result: obtaining a respective confidence embedding that represents a set of confidence features; generating, using a first attention mechanism, a confidence feature vector; generating, using a second attention mechanism, an acoustic context vector; and generating, as output from an output layer of the CEM, a respective confidence output score for each corresponding sub-word unit based on the confidence feature vector and the acoustic feature vector received as input by the output layer of the CEM. For each of the one or more words formed by the sequence of hypothesized sub-word units, the method also includes determining a respective word-level confidence score for the word. The method also includes determining an utterance-level confidence score by aggregating the word-level confidence scores.

    KEY PHRASE SPOTTING
    19.
    发明申请
    KEY PHRASE SPOTTING 有权

    公开(公告)号：US20220199084A1

    公开(公告)日：2022-06-23

    申请号：US17654195

    申请日：2022-03-09

    Applicant: Google LLC

    Inventor： Wei Li ,  Rohit Prakash Prabhavalkar ,  Kanury Kanishka Rao ,  Yanzhang He ,  Ian C. McGraw ,  Anton Bakhtin

    IPC: G10L15/22 ,  G10L15/06 ,  G10L15/18 ,  G10L19/00 ,  G10L15/02

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting utterances of a key phrase in an audio signal. One of the methods includes receiving, by a key phrase spotting system, an audio signal encoding one or more utterances; while continuing to receive the audio signal, generating, by the key phrase spotting system, an attention output using an attention mechanism that is configured to compute the attention output based on a series of encodings generated by an encoder comprising one or more neural network layers, generating, by the key phrase spotting system and using attention output, output that indicates whether the audio signal likely encodes the key phrase; and providing, by the key phrase spotting system, the output that indicates whether the audio signal likely encodes the key phrase.

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