Patents by Inventor Rami Botros
Rami Botros 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: 20230352006Abstract: A RNN-T model includes a prediction network configured to, at each of a plurality of times steps subsequent to an initial time step, receive a sequence of non-blank symbols. For each non-blank symbol the prediction network is also configured to generate, using a shared embedding matrix, an embedding of the corresponding non-blank symbol, assign a respective position vector to the corresponding non-blank symbol, and weight the embedding proportional to a similarity between the embedding and the respective position vector. The prediction network is also configured to generate a single embedding vector at the corresponding time step. The RNN-T model also includes a joint network configured to, at each of the plurality of time steps subsequent to the initial time step, receive the single embedding vector generated as output from the prediction network at the corresponding time step and generate a probability distribution over possible speech recognition hypotheses.Type: ApplicationFiled: July 6, 2023Publication date: November 2, 2023Applicant: Google LLCInventors: Rami Botros, Tara Sainath
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Publication number: 20230343328Abstract: 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.Type: ApplicationFiled: June 16, 2023Publication date: October 26, 2023Applicant: Google LLCInventors: Tara Sainath, Arun Narayanan, Rami Botros, Yanzhang He, Ehsan Variani, Cyril Allauzen, David Rybach, Ruoming Pang, Trevor Strohman
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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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Patent number: 11727920Abstract: A RNN-T model includes a prediction network configured to, at each of a plurality of times steps subsequent to an initial time step, receive a sequence of non-blank symbols. For each non-blank symbol the prediction network is also configured to generate, using a shared embedding matrix, an embedding of the corresponding non-blank symbol, assign a respective position vector to the corresponding non-blank symbol, and weight the embedding proportional to a similarity between the embedding and the respective position vector. The prediction network is also configured to generate a single embedding vector at the corresponding time step. The RNN-T model also includes a joint network configured to, at each of the plurality of time steps subsequent to the initial time step, receive the single embedding vector generated as output from the prediction network at the corresponding time step and generate a probability distribution over possible speech recognition hypotheses.Type: GrantFiled: May 26, 2021Date of Patent: August 15, 2023Assignee: Google LLCInventors: Rami Botros, Tara Sainath
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Patent number: 11715458Abstract: 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.Type: GrantFiled: May 10, 2021Date of Patent: August 1, 2023Assignee: Google LLCInventors: Tara Sainath, Arun Narayanan, Rami Botros, Yanzhang He, Ehsan Variani, Cyril Allauzen, David Rybach, Ruoming Pang, Trevor Strohman
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Publication number: 20230130634Abstract: A computer-implemented method includes receiving a sequence of acoustic frames as input to an automatic speech recognition (ASR) model. Here, the ASR model includes a causal encoder and a decoder. The method also includes generating, by the causal encoder, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The method also includes generating, by the decoder, a first probability distribution over possible speech recognition hypotheses. Here, the causal encoder includes a stack of causal encoder layers each including a Recurrent Neural Network (RNN) Attention-Performer module that applies linear attention.Type: ApplicationFiled: September 29, 2022Publication date: April 27, 2023Applicant: Google LLCInventors: Tara N. Sainath, Rami Botros, Anmol Gulati, Krzysztof Choromanski, Ruoming Pang, Trevor Strohman, Weiran Wang, Jiahui Yu
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Publication number: 20220310071Abstract: A RNN-T model includes a prediction network configured to, at each of a plurality of times steps subsequent to an initial time step, receive a sequence of non-blank symbols. For each non-blank symbol the prediction network is also configured to generate, using a shared embedding matrix, an embedding of the corresponding non-blank symbol, assign a respective position vector to the corresponding non-blank symbol, and weight the embedding proportional to a similarity between the embedding and the respective position vector. The prediction network is also configured to generate a single embedding vector at the corresponding time step. The RNN-T model also includes a joint network configured to, at each of the plurality of time steps subsequent to the initial time step, receive the single embedding vector generated as output from the prediction network at the corresponding time step and generate a probability distribution over possible speech recognition hypotheses.Type: ApplicationFiled: May 26, 2021Publication date: September 29, 2022Applicant: Google LLCInventors: Rami Botros, Tara Sainath
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Publication number: 20220310062Abstract: 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.Type: ApplicationFiled: May 10, 2021Publication date: September 29, 2022Applicant: Google LLCInventors: Tara Sainath, Arun Narayanan, Rami Botros, Yangzhang He, Ehsan Variani, Cyrill Allauzen, David Rybach, Ruorning Pang, Trevor Strohman
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Patent number: 10170110Abstract: A method for ranking candidate speech recognition results includes generating, with a controller, a plurality of feature vectors for the candidate speech recognition results, each feature vector including one or more of trigger pair features, a confidence score feature, and word-level features. The method further includes providing the plurality of feature vectors as inputs to a neural network, generating a plurality of ranking scores corresponding to the plurality of feature vectors for the plurality of candidate speech recognition results based on an output layer of the neural network, and operating the automated system using the candidate speech recognition result in the plurality of candidate speech recognition results corresponding to a highest ranking score in the plurality of ranking scores as input.Type: GrantFiled: November 17, 2016Date of Patent: January 1, 2019Assignee: Robert Bosch GmbHInventors: Zhengyu Zhou, Rami Botros
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Publication number: 20180137857Abstract: A method for ranking candidate speech recognition results includes generating, with a controller, a plurality of feature vectors for the candidate speech recognition results, each feature vector including one or more of trigger pair features, a confidence score feature, and word-level features. The method further includes providing the plurality of feature vectors as inputs to a neural network, generating a plurality of ranking scores corresponding to the plurality of feature vectors for the plurality of candidate speech recognition results based on an output layer of the neural network, and operating the automated system using the candidate speech recognition result in the plurality of candidate speech recognition results corresponding to a highest ranking score in the plurality of ranking scores as input.Type: ApplicationFiled: November 17, 2016Publication date: May 17, 2018Applicant: Robert Bosch GmbHInventors: Zhengyu Zhou, Rami Botros