Patents by Inventor Ian McGraw
Ian McGraw has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240153495Abstract: A method includes receiving a training dataset that includes one or more spoken training utterances for training an automatic speech recognition (ASR) model. Each spoken training utterance in the training dataset paired with a corresponding transcription and a corresponding target sequence of auxiliary tokens. For each spoken training utterance, the method includes generating a speech recognition hypothesis for a corresponding spoken training utterance, determining a speech recognition loss based on the speech recognition hypothesis and the corresponding transcription, generating a predicted auxiliary token for the corresponding spoken training utterance, and determining an auxiliary task loss based on the predicted auxiliary token and the corresponding target sequence of auxiliary tokens. The method also includes the ASR model jointly on the speech recognition loss and the auxiliary task loss determined for each spoken training utterance.Type: ApplicationFiled: October 26, 2023Publication date: May 9, 2024Applicant: Google LLCInventors: Weiran Wang, Ding Zhao, Shaojin Ding, Hao Zhang, Shuo-yiin Chang, David Johannes Rybach, Tara N. Sainath, Yanzhang He, Ian McGraw, Shankar Kumar
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Publication number: 20230326461Abstract: An automated speech recognition (ASR) model includes a first encoder, a first encoder, a second encoder, and a second 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 first decoder receives, as input, the first higher order feature representation generated by the first encoder, and generates a first probability distribution over possible speech recognition hypotheses. The second encoder receives, as input, the first higher order feature representation generated by the first encoder, and generates a second higher order feature representation for a corresponding first higher order feature frame. The second decoder receives, as input, the second higher order feature representation generated by the second encoder, and generates a second probability distribution over possible speech recognition hypotheses.Type: ApplicationFiled: March 13, 2023Publication date: October 12, 2023Applicant: Google LLCInventors: Shaojin Ding, Yangzhang He, Xin Wang, Weiran Wang, Trevor Strohman, Tara N. Sainath, Rohit Parkash Prabhavalkar, Robert David, Rina Panigrahy, Rami Botros, Qiao Liang, Ian Mcgraw, Ding Zhao, Dongseong Hwang
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Publication number: 20230298591Abstract: A computer-implemented method includes receiving a sequence of acoustic frames corresponding to an utterance and generating a reference speaker embedding for the utterance. The method also includes receiving a target speaker embedding for a target speaker and generating feature-wise linear modulation (FiLM) parameters including a scaling vector and a shifting vector based on the target speaker embedding. The method also includes generating an affine transformation output that scales and shifts the reference speaker embedding based on the FiLM parameters. The method also includes generating a classification output indicating whether the utterance was spoken by the target speaker based on the affine transformation output.Type: ApplicationFiled: March 17, 2023Publication date: September 21, 2023Applicant: Google LLCInventors: Shaojin Ding, Rajeev Rikhye, Qiao Liang, Yanzhang He, Quan Wang, Arun Narayanan, Tom O'Malley, Ian McGraw
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Patent number: 11610586Abstract: 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.Type: GrantFiled: February 23, 2021Date of Patent: March 21, 2023Assignee: Google LLCInventors: David Qiu, Qiujia Li, Yanzhang He, Yu Zhang, Bo Li, Liangliang Cao, Rohit Prabhavalkar, Deepti Bhatia, Wei Li, Ke Hu, Tara Sainath, Ian Mcgraw
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Publication number: 20220310080Abstract: A method including receiving a speech recognition result corresponding to a transcription of an utterance spoken by a user. For each sub-word unit in a sequence of hypothesized sub-word units of the speech recognition result, using a confidence estimation module to: obtain a respective confidence embedding associated with the corresponding output step when the corresponding sub-word unit was output from the first speech recognizer; generate a confidence feature vector; generate an acoustic context vector; and generate a respective confidence output score for the corresponding sub-word unit based on the confidence feature vector and the acoustic feature vector received as input by the output layer of the confidence estimation module. The method also includes determining, based on the respective confidence output score generated for each sub-word unit in the sequence of hypothesized sub-word units, an utterance-level confidence score for the transcription of the utterance.Type: ApplicationFiled: December 11, 2021Publication date: September 29, 2022Applicant: Google LLCInventors: David Qiu, Yanzhang He, Yu Zhang, Qiujia Li, Liangliang Cao, Ian McGraw
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Publication number: 20220270597Abstract: 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.Type: ApplicationFiled: February 23, 2021Publication date: August 25, 2022Applicant: Google LLCInventors: David Qiu, Qiujia Li, Yanzhang He, Yu Zhang, Bo Li, Liangliang Cao, Rohit Prabhavalkar, Deepti Bhatia, Wei Li, Ke Hu, Tara Sainath, Ian Mcgraw
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Publication number: 20060212812Abstract: A note-taking application is provided which includes a free-form selection tool. In certain embodiments, a particular selection may be based in part on the speed or velocity of a user selection. The free-form selection tool is configured to provide more or less precise selections based in part on the speed of a selection. The free-form selection tool is further operable to recognize when a user is attempting to retry a selection.Type: ApplicationFiled: March 21, 2005Publication date: September 21, 2006Applicant: Microsoft CorporationInventors: Alex Simmons, Ian McGraw, Owen Braun, Benoit Barabe, Peter Engrav