Patents by Inventor Awni Hannun
Awni Hannun 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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Patent number: 11562733Abstract: Presented herein are embodiments of state-of-the-art speech recognition systems developed using end-to-end deep learning. In embodiments, the model architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, embodiments of the system do not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learn a function that is robust to such effects. Neither a phoneme dictionary, nor even the concept of a “phoneme,” is needed. Embodiments include a well-optimized recurrent neural network (RNN) training system that can use multiple GPUs, as well as a set of novel data synthesis techniques that allows for a large amount of varied data for training to be efficiently obtained.Type: GrantFiled: August 15, 2019Date of Patent: January 24, 2023Assignee: BAIDU USA LLCInventors: Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Gregory Diamos, Erich Eisen, Ryan Prenger, Sanjeev Satheesh, Shubhabrata Sengupta, Adam Coates, Andrew Ng
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Patent number: 10832120Abstract: Systems and methods for a multi-core optimized Recurrent Neural Network (RNN) architecture are disclosed. The various architectures affect communication and synchronization operations according to the Multi-Bulk-Synchronous-Parallel (MBSP) model for a given processor. The resulting family of network architectures, referred to as MBSP-RNNs, perform similarly to a conventional RNNs having the same number of parameters, but are substantially more efficient when mapped onto a modern general purpose processor. Due to the large gain in computational efficiency, for a fixed computational budget, MBSP-RNNs outperform RNNs at applications such as end-to-end speech recognition.Type: GrantFiled: April 5, 2016Date of Patent: November 10, 2020Assignee: Baidu USA LLCInventors: Gregory Diamos, Awni Hannun, Bryan Catanzaro, Dario Amodei, Erich Elsen, Jesse Engel, Shubhabrata Sengupta
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Patent number: 10540957Abstract: Presented herein are embodiments of state-of-the-art speech recognition systems developed using end-to-end deep learning. In embodiments, the model architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, embodiments of the system do not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learn a function that is robust to such effects. A phoneme dictionary, nor even the concept of a “phoneme,” is needed. Embodiments include a well-optimized recurrent neural network (RNN) training system that can use multiple GPUs, as well as a set of novel data synthesis techniques that allows for a large amount of varied data for training to be efficiently obtained.Type: GrantFiled: June 9, 2015Date of Patent: January 21, 2020Assignee: BAIDU USA LLCInventors: Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Gregory Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubhabrata Sengupta, Adam Coates, Andrew Y. Ng
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Publication number: 20190371298Abstract: Presented herein are embodiments of state-of-the-art speech recognition systems developed using end-to-end deep learning. In embodiments, the model architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, embodiments of the system do not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learn a function that is robust to such effects. A phoneme dictionary, nor even the concept of a “phoneme,” is needed. Embodiments include a well-optimized recurrent neural network (RNN) training system that can use multiple GPUs, as well as a set of novel data synthesis techniques that allows for a large amount of varied data for training to be efficiently obtained.Type: ApplicationFiled: August 15, 2019Publication date: December 5, 2019Applicant: BAIDU USA LLCInventors: Awni HANNUN, Carl CASE, Jared Casper, Bryan Catanzaro, Gregory Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubhabrata Sengupta, Adam Coates, Andrew Ng
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Patent number: 10332509Abstract: Embodiments of end-to-end deep learning systems and methods are disclosed to recognize speech of vastly different languages, such as English or Mandarin Chinese. In embodiments, the entire pipelines of hand-engineered components are replaced with neural networks, and the end-to-end learning allows handling a diverse variety of speech including noisy environments, accents, and different languages. Using a trained embodiment and an embodiment of a batch dispatch technique with GPUs in a data center, an end-to-end deep learning system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.Type: GrantFiled: November 21, 2016Date of Patent: June 25, 2019Assignee: Baidu USA, LLCInventors: Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Erich Elsen, Jesse Engel, Christopher Fougner, Xu Han, Awni Hannun, Ryan Prenger, Sanjeev Satheesh, Shubhabrata Sengupta, Dani Yogatama, Chong Wang, Jun Zhan, Zhenyao Zhu, Dario Amodei
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Patent number: 10319374Abstract: Embodiments of end-to-end deep learning systems and methods are disclosed to recognize speech of vastly different languages, such as English or Mandarin Chinese. In embodiments, the entire pipelines of hand-engineered components are replaced with neural networks, and the end-to-end learning allows handling a diverse variety of speech including noisy environments, accents, and different languages. Using a trained embodiment and an embodiment of a batch dispatch technique with GPUs in a data center, an end-to-end deep learning system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.Type: GrantFiled: November 21, 2016Date of Patent: June 11, 2019Assignee: Baidu USA, LLCInventors: Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Erich Elsen, Jesse Engel, Christopher Fougner, Xu Han, Awni Hannun, Ryan Prenger, Sanjeev Satheesh, Shubhabrata Sengupta, Dani Yogatama, Chong Wang, Jun Zhan, Zhenyao Zhu, Dario Amodei
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Publication number: 20170169326Abstract: Systems and methods for a multi-core optimized Recurrent Neural Network (RNN) architecture are disclosed. The various architectures affect communication and synchronization operations according to the Multi-Bulk-Synchronous-Parallel (MBSP) model for a given processor. The resulting family of network architectures, referred to as MBSP-RNNs, perform similarly to a conventional RNNs having the same number of parameters, but are substantially more efficient when mapped onto a modern general purpose processor. Due to the large gain in computational efficiency, for a fixed computational budget, MBSP-RNNs outperform RNNs at applications such as end-to-end speech recognition.Type: ApplicationFiled: April 5, 2016Publication date: June 15, 2017Applicant: Baidu USA LLCInventors: Gregory Diamos, Awni Hannun, Bryan Catanzaro, Dario Amodei, Erich Elsen, Jesse Engel, Shubhabrata Sengupta
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Publication number: 20170148431Abstract: Embodiments of end-to-end deep learning systems and methods are disclosed to recognize speech of vastly different languages, such as English or Mandarin Chinese. In embodiments, the entire pipelines of hand-engineered components are replaced with neural networks, and the end-to-end learning allows handling a diverse variety of speech including noisy environments, accents, and different languages. Using a trained embodiment and an embodiment of a batch dispatch technique with GPUs in a data center, an end-to-end deep learning system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.Type: ApplicationFiled: November 21, 2016Publication date: May 25, 2017Applicant: Baidu USA LLCInventors: Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Erich Elsen, Jesse Engel, Christopher Fougner, Xu Han, Awni Hannun, Ryan Prenger, Sanjeev Satheesh, Shubhabrata Sengupta, Dani Yogatama, Chong Wang, Jun Zhan, Zhenyao Zhu, Dario Amodei
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Publication number: 20170148433Abstract: Embodiments of end-to-end deep learning systems and methods are disclosed to recognize speech of vastly different languages, such as English or Mandarin Chinese. In embodiments, the entire pipelines of hand-engineered components are replaced with neural networks, and the end-to-end learning allows handling a diverse variety of speech including noisy environments, accents, and different languages. Using a trained embodiment and an embodiment of a batch dispatch technique with GPUs in a data center, an end-to-end deep learning system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.Type: ApplicationFiled: November 21, 2016Publication date: May 25, 2017Applicant: Baidu USA LLCInventors: Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Erich Elsen, Jesse Engel, Christopher Fougner, Xu Han, Awni Hannun, Ryan Prenger, Sanjeev Satheesh, Shubhabrata Sengupta, Dani Yogatama, Chong Wang, Jun Zhan, Zhenyao Zhu, Dario Amodei
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Publication number: 20160171974Abstract: Presented herein are embodiments of state-of-the-art speech recognition systems developed using end-to-end deep learning. In embodiments, the model architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, embodiments of the system do not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learn a function that is robust to such effects. A phoneme dictionary, nor even the concept of a “phoneme,” is needed. Embodiments include a well-optimized recurrent neural network (RNN) training system that can use multiple GPUs, as well as a set of novel data synthesis techniques that allows for a large amount of varied data for training to be efficiently obtained.Type: ApplicationFiled: June 9, 2015Publication date: June 16, 2016Applicant: BAIDU USA LLCInventors: Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Gregory Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubhabrata Sengupta, Adam Coates, Andrew Y. Ng