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公开(公告)号：US10140980B2
公开(公告)日：2018-11-27
申请号：US15386979
申请日：2016-12-21
Applicant: Google LLC
Inventor： Samuel Bengio , Mirko Visontai , Christopher Walter George Thornton , Michiel A. U. Bacchiani , Tara N. Sainath , Ehsan Variani , Izhak Shafran
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.
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公开(公告)号：US20240371363A1
公开(公告)日：2024-11-07
申请号：US18772263
申请日：2024-07-15
Applicant: Google LLC
Inventor： Tara Sainath , Arun Narayanan , Rami Botros , Yanzhang He , Ehsan Variani , Cyril Allauzen , David Rybach , Ruoming Pang , Trevor Strohman
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.
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公开(公告)号：US12051404B2
公开(公告)日：2024-07-30
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
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.
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公开(公告)号：US11922322B2
公开(公告)日：2024-03-05
申请号：US18161479
申请日：2023-01-30
Applicant: Google LLC
Inventor： Mitchel Weintraub , Ananda Theertha Suresh , Ehsan Variani
CPC classification number: G06N3/084 , G06F18/2431 , G06N20/00
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.
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公开(公告)号：US20220310062A1
公开(公告)日：2022-09-29
申请号：US17316198
申请日：2021-05-10
Applicant: Google LLC
Inventor： Tara Sainath , Arun Narayanan , Rami Botros , Yangzhang He , Ehsan Variani , Cyrill Allauzen , David Rybach , Ruorning Pang , Trevor Strohman
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.
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Patent Agency Ranking
公开(公告)号：US11062725B2
公开(公告)日：2021-07-13
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: G10L15/16 , G10L25/30 , G10L21/028 , G10L21/0388 , G10L19/008 , G10L15/20 , G10L21/0208 , G10L21/0216
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.
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公开(公告)号：US10224058B2
公开(公告)日：2019-03-05
申请号：US15350293
申请日：2016-11-14
Applicant: Google LLC
Inventor： Ehsan Variani , Kevin William Wilson , Ron J. Weiss , Tara N. Sainath , Arun Narayanan
IPC: G10L15/16 , G10L25/30 , G10L21/028 , G10L21/0388 , G10L19/008 , G10L15/20 , G10L21/0208 , G10L21/0216
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.
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公开(公告)号：US20180174575A1
公开(公告)日：2018-06-21
申请号：US15386979
申请日：2016-12-21
Applicant: Google LLC
Inventor： Samuel Bengio , Mirko Visontai , Christopher Walter George Thornton , Michiel A.U. Bacchiani , Tara N. Sainath , Ehsan Variani , Izhak Shafran
CPC classification number: G10L15/16 , G10H1/00 , G10H2210/036 , G10H2210/046 , G10H2250/235 , G10H2250/311 , G10L15/02 , 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.
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公开(公告)号：US12154581B2
公开(公告)日：2024-11-26
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
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.
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公开(公告)号：US12080283B2
公开(公告)日：2024-09-03
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 , G10L15/00 , 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.
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