Patents by Inventor Guoli YE
Guoli YE 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: 11798535Abstract: Generally discussed herein are devices, systems, and methods for on-device detection of a wake word. A device can include a memory including model parameters that define a custom wake word detection model, the wake word detection model including a recurrent neural network transducer (RNNT) and a lookup table (LUT), the LUT indicating a hidden vector to be provided in response to a phoneme of a user-specified wake word, a microphone to capture audio, and processing circuitry to receive the audio from the microphone, determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word, and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word.Type: GrantFiled: September 14, 2021Date of Patent: October 24, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Emilian Stoimenov, Rui Zhao, Kaustubh Prakash Kalgaonkar, Ivaylo Andreanov Enchev, Khuram Shahid, Anthony Phillip Stark, Guoli Ye, Mahadevan Srinivasan, Yifan Gong, Hosam Adel Khalil
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Patent number: 11790891Abstract: Generally discussed herein are devices, systems, and methods for custom wake word selection assistance. A method can include receiving, at a device, data indicating a custom wake word provided by a user, determining one or more characteristics of the custom wake word, determining that use of the custom wake word will cause more than a threshold rate of false detections based on the characteristics, rejecting the custom wake word as the wake word for accessing a personal assistant in response to determining that use of the custom wake word will cause more than a threshold rate of false detections, and setting the custom wake word as the wake word in response to determining that use of the custom wake word will not cause more than the threshold rate of false detections.Type: GrantFiled: December 1, 2021Date of Patent: October 17, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Emilian Stoimenov, Khuram Shahid, Guoli Ye, Hosam Adel Khalil, Yifan Gong
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Publication number: 20230186919Abstract: Systems, methods, and devices are provided for generating and using text-to-speech (TTS) data for improved speech recognition models. A main model is trained with keyword independent baseline training data. In some instances, acoustic and language model sub-components of the main model are modified with new TTS training data. In some instances, the new TTS training is obtained from a multi-speaker neural TTS system for a keyword that is underrepresented in the baseline training data. In some instances, the new TTS training data is used for pronunciation learning and normalization of keyword dependent confidence scores in keyword spotting (KWS) applications. In some instances, the new TTS training data is used for rapid speaker adaptation in speech recognition models.Type: ApplicationFiled: February 10, 2023Publication date: June 15, 2023Inventors: Guoli YE, Yan HUANG, Wenning WEI, Lei HE, Eva SHARMA, Jian WU, Yao TIAN, Edward C. LIN, Yifan GONG, Rui ZHAO, Jinyu LI, William Maxwell GALE
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Patent number: 11587569Abstract: Systems, methods, and devices are provided for generating and using text-to-speech (TTS) data for improved speech recognition models. A main model is trained with keyword independent baseline training data. In some instances, acoustic and language model sub-components of the main model are modified with new TTS training data. In some instances, the new TTS training is obtained from a multi-speaker neural TTS system for a keyword that is underrepresented in the baseline training data. In some instances, the new TTS training data is used for pronunciation learning and normalization of keyword dependent confidence scores in keyword spotting (KWS) applications. In some instances, the new TTS training data is used for rapid speaker adaptation in speech recognition models.Type: GrantFiled: May 14, 2020Date of Patent: February 21, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Guoli Ye, Yan Huang, Wenning Wei, Lei He, Eva Sharma, Jian Wu, Yao Tian, Edward C. Lin, Yifan Gong, Rui Zhao, Jinyu Li, William Maxwell Gale
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Publication number: 20220254334Abstract: Generally discussed herein are devices, systems, and methods for custom wake word selection assistance. A method can include receiving, at a device, data indicating a custom wake word provided by a user, determining one or more characteristics of the custom wake word, determining that use of the custom wake word will cause more than a threshold rate of false detections based on the characteristics, rejecting the custom wake word as the wake word for accessing a personal assistant in response to determining that use of the custom wake word will cause more than a threshold rate of false detections, and setting the custom wake word as the wake word in response to determining that use of the custom wake word will not cause more than the threshold rate of false detections.Type: ApplicationFiled: December 1, 2021Publication date: August 11, 2022Inventors: Emilian Stoimenov, Khuram Shahid, Guoli Ye, Hosam Adel Khalil, Yifan Gong
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Patent number: 11222622Abstract: Generally discussed herein are devices, systems, and methods for custom wake word selection assistance. A method can include receiving, at a device, data indicating a custom wake word provided by a user, determining one or more characteristics of the custom wake word, determining that use of the custom wake word will cause more than a threshold rate of false detections based on the characteristics, rejecting the custom wake word as the wake word for accessing a personal assistant in response to determining that use of the custom wake word will cause more than a threshold rate of false detections, and setting the custom wake word as the wake word in response to determining that use of the custom wake word will not cause more than the threshold rate of false detections.Type: GrantFiled: July 25, 2019Date of Patent: January 11, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Emilian Stoimenov, Khuram Shahid, Guoli Ye, Hosam Adel Khalil, Yifan Gong
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Publication number: 20210407498Abstract: Generally discussed herein are devices, systems, and methods for on-device detection of a wake word. A device can include a memory including model parameters that define a custom wake word detection model, the wake word detection model including a recurrent neural network transducer (RNNT) and a lookup table (LUT), the LUT indicating a hidden vector to be provided in response to a phoneme of a user-specified wake word, a microphone to capture audio, and processing circuitry to receive the audio from the microphone, determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word, and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word.Type: ApplicationFiled: September 14, 2021Publication date: December 30, 2021Inventors: Emilian Stoimenov, Rui Zhao, Kaustubh Prakash Kalgaonkar, Ivaylo Andreanov Enchev, Khuram Shahid, Anthony Phillip Stark, Guoli Ye, Mahadevan Srinivasan, Yifan Gong, Hosam Adel Khalil
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Publication number: 20210304769Abstract: Systems, methods, and devices are provided for generating and using text-to-speech (TTS) data for improved speech recognition models. A main model is trained with keyword independent baseline training data. In some instances, acoustic and language model sub-components of the main model are modified with new TTS training data. In some instances, the new TTS training is obtained from a multi-speaker neural TTS system for a keyword that is underrepresented in the baseline training data. In some instances, the new TTS training data is used for pronunciation learning and normalization of keyword dependent confidence scores in keyword spotting (KWS) applications. In some instances, the new TTS training data is used for rapid speaker adaptation in speech recognition models.Type: ApplicationFiled: May 14, 2020Publication date: September 30, 2021Inventors: Guoli Ye, Yan Huang, Wenning Wei, Lei He, Eva Sharma, Jian Wu, Yao Tian, Edward C. Lin, Yifan Gong, Rui Zhao, Jinyu Li, William Maxwell Gale
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Patent number: 11132992Abstract: Generally discussed herein are devices, systems, and methods for on-device detection of a wake word. A device can include a memory including model parameters that define a custom wake word detection model, the wake word detection model including a recurrent neural network transducer (RNNT) and a lookup table (LUT), the LUT indicating a hidden vector to be provided in response to a phoneme of a user-specified wake word, a microphone to capture audio, and processing circuitry to receive the audio from the microphone, determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word, and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word.Type: GrantFiled: July 25, 2019Date of Patent: September 28, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Emilian Stoimenov, Rui Zhao, Kaustubh Prakash Kalgaonkar, Ivaylo Andreanov Enchev, Khuram Shahid, Anthony Phillip Stark, Guoli Ye, Mahadevan Srinivasan, Yifan Gong, Hosam Adel Khalil
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Patent number: 10964309Abstract: A CS CTC model may be initialed from a major language CTC model by keeping network hidden weights and replacing output tokens with a union of major and secondary language output tokens. The initialized model may be trained by updating parameters with training data from both languages, and a LID model may also be trained with the data. During a decoding process for each of a series of audio frames, if silence dominates a current frame then a silence output token may be emitted. If silence does not dominate the frame, then a major language output token posterior vector from the CS CTC model may be multiplied with the LID major language probability to create a probability vector from the major language. A similar step is performed for the secondary language, and the system may emit an output token associated with the highest probability across all tokens from both languages.Type: GrantFiled: May 13, 2019Date of Patent: March 30, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Jinyu Li, Guoli Ye, Rui Zhao, Yifan Gong, Ke Li
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Publication number: 20200349924Abstract: Generally discussed herein are devices, systems, and methods for custom wake word selection assistance. A method can include receiving, at a device, data indicating a custom wake word provided by a user, determining one or more characteristics of the custom wake word, determining that use of the custom wake word will cause more than a threshold rate of false detections based on the characteristics, rejecting the custom wake word as the wake word for accessing a personal assistant in response to determining that use of the custom wake word will cause more than a threshold rate of false detections, and setting the custom wake word as the wake word in response to determining that use of the custom wake word will not cause more than the threshold rate of false detections.Type: ApplicationFiled: July 25, 2019Publication date: November 5, 2020Inventors: Emilian Stoimenov, Khuram Shahid, Guoli Ye, Hosam Adel Khalil, Yifan Gong
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Publication number: 20200349927Abstract: Generally discussed herein are devices, systems, and methods for on-device detection of a wake word. A device can include a memory including model parameters that define a custom wake word detection model, the wake word detection model including a recurrent neural network transducer (RNNT) and a lookup table (LUT), the LUT indicating a hidden vector to be provided in response to a phoneme of a user-specified wake word, a microphone to capture audio, and processing circuitry to receive the audio from the microphone, determine, using the wake word detection model, whether the audio includes an utterance of the user-specified wake word, and wake up a personal assistant after determining the audio includes the utterance of the user-specified wake word.Type: ApplicationFiled: July 25, 2019Publication date: November 5, 2020Inventors: Emilian Stoimenov, Rui Zhao, Kaustubh Prakash Kalgaonkar, Ivaylo Andreanov Enchev, Khuram Shahid, Anthony Phillip Stark, Guoli Ye, Mahadevan Srinivasan, Yifan Gong, Hosam Adel Khalil
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Publication number: 20200335082Abstract: A CS CTC model may be initialed from a major language CTC model by keeping network hidden weights and replacing output tokens with a union of major and secondary language output tokens. The initialized model may be trained by updating parameters with training data from both languages, and a LID model may also be trained with the data. During a decoding process for each of a series of audio frames, if silence dominates a current frame then a silence output token may be emitted. If silence does not dominate the frame, then a major language output token posterior vector from the CS CTC model may be multiplied with the LID major language probability to create a probability vector from the major language. A similar step is performed for the secondary language, and the system may emit an output token associated with the highest probability across all tokens from both languages.Type: ApplicationFiled: May 13, 2019Publication date: October 22, 2020Inventors: Jinyu LI, Guoli YE, Rui ZHAO, Yifan GONG, Ke LI
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Patent number: 10629193Abstract: Non-limiting examples of the present disclosure describe advancements in acoustic-to-word modeling that improve accuracy in speech recognition processing through the replacement of out-of-vocabulary (OOV) tokens. During the decoding of speech signals, better accuracy in speech recognition processing is achieved through training and implementation of multiple different solutions that present enhanced speech recognition models. In one example, a hybrid neural network model for speech recognition processing combines a word-based neural network model as a primary model and a character-based neural network model as an auxiliary model. The primary word-based model emits a word sequence, and an output of character-based auxiliary model is consulted at a segment where the word-based model emits an OOV token. In another example, a mixed unit speech recognition model is developed and trained to generate a mixed word and character sequence during decoding of a speech signal without requiring generation of OOV tokens.Type: GrantFiled: March 9, 2018Date of Patent: April 21, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Guoli Ye, James Droppo, Jinyu Li, Rui Zhao, Yifan Gong
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Publication number: 20190279614Abstract: Non-limiting examples of the present disclosure describe advancements in acoustic-to-word modeling that improve accuracy in speech recognition processing through the replacement of out-of-vocabulary (OOV) tokens. During the decoding of speech signals, better accuracy in speech recognition processing is achieved through training and implementation of multiple different solutions that present enhanced speech recognition models. In one example, a hybrid neural network model for speech recognition processing combines a word-based neural network model as a primary model and a character-based neural network model as an auxiliary model. The primary word-based model emits a word sequence, and an output of character-based auxiliary model is consulted at a segment where the word-based model emits an OOV token. In another example, a mixed unit speech recognition model is developed and trained to generate a mixed word and character sequence during decoding of a speech signal without requiring generation of OOV tokens.Type: ApplicationFiled: March 9, 2018Publication date: September 12, 2019Inventors: Guoli YE, James DROPPO, Jinyu LI, Rui ZHAO, Yifan GONG