中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

26 records (took 0.014 seconds)

    Multilingual re-scoring models for automatic speech recognition
    1.
    发明授权
    Multilingual re-scoring models for automatic speech recognition 有权

    公开(公告)号：US12254875B2

    公开(公告)日：2025-03-18

    申请号：US18589220

    申请日：2024-02-27

    Applicant: Google LLC

    Inventor： Neeraj Gaur ,  Tongzhou Chen ,  Ehsan Variani ,  Bhuvana Ramabhadran ,  Parisa Haghani ,  Pedro J. Moreno Mengibar

    IPC: G10L15/197 ,  G10L15/00 ,  G10L15/16 ,  G10L15/22

    Abstract: A method includes receiving a sequence of acoustic frames extracted from audio data corresponding to an utterance. During a first pass, the method includes processing the sequence of acoustic frames to generate N candidate hypotheses for the utterance. During a second pass, and for each candidate hypothesis, the method includes: generating a respective un-normalized likelihood score; generating a respective external language model score; generating a standalone score that models prior statistics of the corresponding candidate hypothesis; and generating a respective overall score for the candidate hypothesis based on the un-normalized likelihood score, the external language model score, and the standalone score. The method also includes selecting the candidate hypothesis having the highest respective overall score from among the N candidate hypotheses as a final transcription of the utterance.

    EFFICIENT STREAMING NON-RECURRENT ON-DEVICE END-TO-END MODEL
    3.
    发明公开
    EFFICIENT STREAMING NON-RECURRENT ON-DEVICE END-TO-END MODEL 审中-公开

    公开(公告)号：US20230343328A1

    公开(公告)日：2023-10-26

    申请号：US18336211

    申请日：2023-06-16

    Applicant: Google LLC

    Inventor： Tara Sainath ,  Arun Narayanan ,  Rami Botros ,  Yanzhang He ,  Ehsan Variani ,  Cyril Allauzen ,  David Rybach ,  Ruoming Pang ,  Trevor Strohman

    IPC: G10L15/06 ,  G10L15/02 ,  G10L15/22 ,  G10L15/30

    CPC classification number: G10L15/063 ,  G10L15/02 ,  G10L15/22 ,  G10L15/30

    Abstract: An ASR model includes a first encoder configured to receive a sequence of acoustic frames and generate a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The ASR model also includes a second encoder configured to receive the first higher order feature representation generated by the first encoder at each of the plurality of output steps and generate a second higher order feature representation for a corresponding first higher order feature frame. The ASR model also includes a decoder configured to receive the second higher order feature representation generated by the second encoder at each of the plurality of output steps and generate a first probability distribution over possible speech recognition hypothesis. The ASR model also includes a language model configured to receive the first probability distribution over possible speech hypothesis and generate a rescored probability distribution.

    Multilingual Re-Scoring Models for Automatic Speech Recognition
    4.
    发明申请
    Multilingual Re-Scoring Models for Automatic Speech Recognition 有权

    公开(公告)号：US20220310081A1

    公开(公告)日：2022-09-29

    申请号：US17701635

    申请日：2022-03-22

    Applicant: Google LLC

    Inventor： Neeraj Gaur ,  Tongzhou Chen ,  Ehsan Variani ,  Bhuvana Ramabhadran ,  Parisa Haghani ,  Pedro J. Moreno Mengibar

    IPC: G10L15/197 ,  G10L15/16 ,  G10L15/22 ,  G10L15/00

    Abstract: A method includes receiving a sequence of acoustic frames extracted from audio data corresponding to an utterance. During a first pass, the method includes processing the sequence of acoustic frames to generate N candidate hypotheses for the utterance. During a second pass, and for each candidate hypothesis, the method includes generating a respective un-normalized likelihood score; generating a respective external language model score; generating a standalone score that models prior statistics of the corresponding candidate hypothesis, and generating a respective overall score for the candidate hypothesis based on the un-normalized likelihood score, the external language model score, and the standalone score. The method also includes selecting the candidate hypothesis having the highest respective overall score from among the N candidate hypotheses as a final transcription of the utterance.

    Cascaded Encoders for Simplified Streaming and Non-Streaming ASR
    5.
    发明申请
    Cascaded Encoders for Simplified Streaming and Non-Streaming ASR 有权

    公开(公告)号：US20220122622A1

    公开(公告)日：2022-04-21

    申请号：US17237021

    申请日：2021-04-21

    Applicant: Google LLC

    Inventor： Arun Narayanan ,  Tara Sainath ,  Chung-Cheng Chiu ,  Ruoming Pang ,  Rohit Prabhavalkar ,  Jiahui Yu ,  Ehsan Variani ,  Trevor Strohman

    IPC: G10L19/16 ,  G10L15/00 ,  G10L25/30 ,  G06N3/08

    Abstract: An automated speech recognition (ASR) model includes a first encoder, a second encoder, and a decoder. The first encoder receives, as input, a sequence of acoustic frames, and generates, at each of a plurality of output steps, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The second encoder receives, as input, the first higher order feature representation generated by the first encoder at each of the plurality of output steps, and generates, at each of the plurality of output steps, a second higher order feature representation for a corresponding first higher order feature frame. The decoder receives, as input, the second higher order feature representation generated by the second encoder at each of the plurality of output steps, and generates, at each of the plurality of time steps, a first probability distribution over possible speech recognition hypotheses.

    COMPLEX LINEAR PROJECTION FOR ACOUSTIC MODELING
    6.
    发明申请
    COMPLEX LINEAR PROJECTION FOR ACOUSTIC MODELING 审中-公开

    公开(公告)号：US20200286468A1

    公开(公告)日：2020-09-10

    申请号：US16879322

    申请日：2020-05-20

    Applicant: Google LLC

    Inventor： Samuel Bengio ,  Mirko Visontai ,  Christopher Walter George Thornton ,  Tara N. Sainath ,  Ehsan Variani ,  Izhak Shafran ,  Michiel A.u. Bacchiani

    IPC: G10L15/16 ,  G10L15/02 ,  G10H1/00 ,  G10L19/02

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using complex linear projection are disclosed. In one aspect, a method includes the actions of receiving audio data corresponding to an utterance. The method further includes generating frequency domain data using the audio data. The method further includes processing the frequency domain data using complex linear projection. The method further includes providing the processed frequency domain data to a neural network trained as an acoustic model. The method further includes generating a transcription for the utterance that is determined based at least on output that the neural network provides in response to receiving the processed frequency domain data.

    Multilingual Re-Scoring Models for Automatic Speech Recognition
    8.
    发明申请
    Multilingual Re-Scoring Models for Automatic Speech Recognition 有权

    公开(公告)号：US20240420692A1

    公开(公告)日：2024-12-19

    申请号：US18818010

    申请日：2024-08-28

    Applicant: Google LLC

    Inventor： Neeraj Gaur ,  Tongzhou Chen ,  Ehsan Variani ,  Bhuvana Ramabhadran ,  Parisa Haghani ,  Pedro J. Moreno Mengibar

    IPC: G10L15/197 ,  G10L15/00 ,  G10L15/16 ,  G10L15/22

    Abstract: A method includes receiving a sequence of acoustic frames extracted from audio data corresponding to an utterance. During a first pass, the method includes processing the sequence of acoustic frames to generate N candidate hypotheses for the utterance. During a second pass, and for each candidate hypothesis, the method includes: generating a respective un-normalized likelihood score; generating a respective external language model score; generating a standalone score that models prior statistics of the corresponding candidate hypothesis; and generating a respective overall score for the candidate hypothesis based on the un-normalized likelihood score, the external language model score, and the standalone score. The method also includes selecting the candidate hypothesis having the highest respective overall score from among the N candidate hypotheses as a final transcription of the utterance.

    ENHANCED MULTI-CHANNEL ACOUSTIC MODELS
    10.
    发明申请
    ENHANCED MULTI-CHANNEL ACOUSTIC MODELS 审中-公开

    公开(公告)号：US20190259409A1

    公开(公告)日：2019-08-22

    申请号：US16278830

    申请日：2019-02-19

    Applicant: Google LLC

    Inventor： Ehsan Variani ,  Kevin William Wilson ,  Ron J. Weiss ,  Tara N. Sainath ,  Arun Narayanan

    IPC: G10L25/30 ,  G10L21/028 ,  G10L19/008 ,  G10L15/20 ,  G10L15/16 ,  G10L21/0388

    Abstract: This specification describes computer-implemented methods and systems. One method includes receiving, by a neural network of a speech recognition system, first data representing a first raw audio signal and second data representing a second raw audio signal. The first raw audio signal and the second raw audio signal describe audio occurring at a same period of time. The method further includes generating, by a spatial filtering layer of the neural network, a spatial filtered output using the first data and the second data, and generating, by a spectral filtering layer of the neural network, a spectral filtered output using the spatial filtered output. Generating the spectral filtered output comprises processing frequency-domain data representing the spatial filtered output. The method still further includes processing, by one or more additional layers of the neural network, the spectral filtered output to predict sub-word units encoded in both the first raw audio signal and the second raw audio signal.

Patent Agency Ranking