公开(公告)号：US20240203409A1

公开(公告)日：2024-06-20

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

CPC classification number: G10L15/197 ,  G10L15/005 ,  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.

公开(公告)号：US11715458B2

公开(公告)日：2023-08-01

申请号：US17316198

申请日：2021-05-10

Applicant: Google LLC

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

IPC: G10L15/00 ,  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.

公开(公告)号：US20230186096A1

公开(公告)日：2023-06-15

申请号：US18161479

申请日：2023-01-30

Applicant: Google LLC

Inventor： Mitchel Weintraub ,  Ananda Theertha Suresh ,  Ehsan Variani

IPC: G06N3/084 ,  G06N20/00 ,  G06F18/2431

CPC classification number: G06N3/084 ,  G06N20/00 ,  G06F18/2431

Abstract: Aspects of the present disclosure enable humanly-specified relationships to contribute to a mapping that enables compression of the output structure of a machine-learned model. An exponential model such as a maximum entropy model can leverage a machine-learned embedding and the mapping to produce a classification output. In such fashion, the feature discovery capabilities of machine-learned models (e.g., deep networks) can be synergistically combined with relationships developed based on human understanding of the structural nature of the problem to be solved, thereby enabling compression of model output structures without significant loss of accuracy. These compressed models provide improved applicability to “on device” or other resource-constrained scenarios.

公开(公告)号：US11568260B2

公开(公告)日：2023-01-31

申请号：US16654425

申请日：2019-10-16

Applicant: Google LLC

Inventor： Mitchel Weintraub ,  Ananda Theertha Suresh ,  Ehsan Variani

IPC: G06N20/00 ,  G06N99/00 ,  G06N3/08 ,  G06K9/62 ,  G06N20/10

Abstract: Aspects of the present disclosure enable humanly-specified relationships to contribute to a mapping that enables compression of the output structure of a machine-learned model. An exponential model such as a maximum entropy model can leverage a machine-learned embedding and the mapping to produce a classification output. In such fashion, the feature discovery capabilities of machine-learned models (e.g., deep networks) can be synergistically combined with relationships developed based on human understanding of the structural nature of the problem to be solved, thereby enabling compression of model output structures without significant loss of accuracy. These compressed models provide improved applicability to “on device” or other resource-constrained scenarios.

公开(公告)号：US20210295859A1

公开(公告)日：2021-09-23

申请号：US17303822

申请日：2021-06-08

Applicant: Google LLC

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

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

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.

公开(公告)号：US10714078B2

公开(公告)日：2020-07-14

申请号：US16171629

申请日：2018-10-26

Applicant: Google LLC

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

IPC: G10L15/16 ,  G10L15/02 ,  G10H1/00 ,  G10L19/02 ,  G10L17/18

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.