Patents by Inventor Maisy Wieman
Maisy Wieman 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).
-
Publication number: 20240046918Abstract: A system and method invoke virtual assistant action, which may comprise an argument. From audio, a probability of an intent is inferred. A probability of a domain and a plurality of variable values may also be inferred. Invoking the action is in response to the intent probability exceeding a threshold. Invoking the action may also be in response to the domain probability exceeding a threshold, a variable value probability exceeding a threshold, detecting an end of utterance, and a specific amount of time having elapsed. The intent probability may increase when the audio includes speech of words with the same meaning in multiple natural languages. Invoking the action may also be conditional on the variable value exceeding its threshold within a certain period of time of the intent probability exceeding its threshold.Type: ApplicationFiled: September 26, 2023Publication date: February 8, 2024Applicant: SoundHound AI IP, LLCInventors: Sudharsan Krishnaswamy, Maisy Wieman, Jonah Probell
-
Publication number: 20230419970Abstract: A neural speech-to-meaning system is trained on speech audio expressing specific intents. The system receives speech audio and produces indications of when the speech in the audio matches the intent. Intents may include variables that can have a large range of values, such as the names of places. The neural speech-to-meaning system simultaneously recognizes enumerated values of variables and general intents. Recognized variable values can serve as arguments to API requests made in response to recognized intents. Accordingly, neural speech-to-meaning supports voice virtual assistants that serve users based on API hits.Type: ApplicationFiled: September 5, 2023Publication date: December 28, 2023Applicant: SoundHound AI IP, LLCInventors: Sudharsan Krishnaswamy, Maisy Wieman, Jonah Probell
-
Patent number: 11769488Abstract: A system and method invoke virtual assistant action, which may comprise an argument. From audio, a probability of an intent is inferred. A probability of a domain and a plurality of variable values may also be inferred. Invoking the action is in response to the intent probability exceeding a threshold. Invoking the action may also be in response to the domain probability exceeding a threshold, a variable value probability exceeding a threshold, detecting an end of utterance, and a specific amount of time having elapsed. The intent probability may increase when the audio includes speech of words with the same meaning in multiple natural languages. Invoking the action may also be conditional on the variable value exceeding its threshold within a certain period of time of the intent probability exceeding its threshold.Type: GrantFiled: March 3, 2022Date of Patent: September 26, 2023Assignee: SoundHound AI IP, LLCInventors: Sudharsan Krishnaswamy, Maisy Wieman, Jonah Probell
-
Patent number: 11749281Abstract: A neural speech-to-meaning system is trained on speech audio expressing specific intents. The system receives speech audio and produces indications of when the speech in the audio matches the intent. Intents may include variables that can have a large range of values, such as the names of places. The neural speech-to-meaning system simultaneously recognizes enumerated values of variables and general intents. Recognized variable values can serve as arguments to API requests made in response to recognized intents. Accordingly, neural speech-to-meaning supports voice virtual assistants that serve users based on API hits.Type: GrantFiled: December 4, 2019Date of Patent: September 5, 2023Assignee: SoundHound AI IP, LLCInventors: Sudharsan Krishnaswamy, Maisy Wieman, Jonah Probell
-
Patent number: 11392833Abstract: An audio processing system is described. The audio processing system uses a convolutional neural network architecture to process audio data, a recurrent neural network architecture to process at least data derived from an output of the convolutional neural network architecture, and a feed-forward neural network architecture to process at least data derived from an output of the recurrent neural network architecture. The feed-forward neural network architecture is configured to output classification scores for a plurality of sound units associated with speech. The classification scores indicate a presence of one or more sound units in the audio data. The convolutional neural network architecture has a plurality of convolutional groups arranged in series, where a convolutional group includes a combination of two data mappings arranged in parallel.Type: GrantFiled: February 13, 2020Date of Patent: July 19, 2022Assignee: SoundHound, Inc.Inventors: Maisy Wieman, Andrew Carl Spencer, Zìlì L{hacek over (i)}, Cristina Vasconcelos
-
Publication number: 20220189464Abstract: A system and method invoke virtual assistant action, which may comprise an argument. From audio, a probability of an intent is inferred. A probability of a domain and a plurality of variable values may also be inferred. Invoking the action is in response to the intent probability exceeding a threshold. Invoking the action may also be in response to the domain probability exceeding a threshold, a variable value probability exceeding a threshold, detecting an end of utterance, and a specific amount of time having elapsed. The intent probability may increase when the audio includes speech of words with the same meaning in multiple natural languages. Invoking the action may also be conditional on the variable value exceeding its threshold within a certain period of time of the intent probability exceeding its threshold.Type: ApplicationFiled: March 3, 2022Publication date: June 16, 2022Applicant: SoundHound, Inc.Inventors: Sudharsan KRISHNASWAMY, Maisy WIEMAN, Jonah PROBELL
-
Patent number: 11308938Abstract: To train a speech recognizer, such as for recognizing variables in a neural speech-to-meaning system, compute, within an embedding space, a range of vectors of features of natural speech. Generate parameter sets for speech synthesis and synthesis speech according to the parameters. Analyze the synthesized speech to compute vectors in the embedding space. Using a cost function that favors an even spread (minimal clustering) generates a multiplicity of speech synthesis parameter sets. Using the multiplicity of parameter sets, generate a multiplicity of speech of known words that can be used as training data for speech recognition.Type: GrantFiled: December 5, 2019Date of Patent: April 19, 2022Assignee: SoundHound, Inc.Inventors: Maisy Wieman, Jonah Probell, Sudharsan Krishnaswamy
-
Publication number: 20210256386Abstract: An audio processing system is described. The audio processing system uses a convolutional neural network architecture to process audio data, a recurrent neural network architecture to process at least data derived from an output of the convolutional neural network architecture, and a feed-forward neural network architecture to process at least data derived from an output of the recurrent neural network architecture. The feed-forward neural network architecture is configured to output classification scores for a plurality of sound units associated with speech. The classification scores indicate a presence of one or more sound units in the audio data. The convolutional neural network architecture has a plurality of convolutional groups arranged in series, where a convolutional group includes a combination of two data mappings arranged in parallel.Type: ApplicationFiled: February 13, 2020Publication date: August 19, 2021Applicant: SoundHound, Inc.Inventors: Maisy Wieman, Andrew Carl Spencer, Zìlì Li, Cristina Vasconcelos
-
Publication number: 20210174806Abstract: A neural speech-to-meaning system is trained on speech audio expressing specific intents. The system receives speech audio and produces indications of when the speech in the audio matches the intent. Intents may include variables that can have a large range of values, such as the names of places. The neural speech-to-meaning system simultaneously recognizes enumerated values of variables and general intents. Recognized variable values can serve as arguments to API requests made in response to recognized intents. Accordingly, neural speech-to-meaning supports voice virtual assistants that serve users based on API hits.Type: ApplicationFiled: December 4, 2019Publication date: June 10, 2021Applicant: SoundHound, Inc.Inventors: Sudharsan Krishnaswamy, Maisy Wieman, Jonah Probell
-
Publication number: 20210174783Abstract: To train a speech recognizer, such as for recognizing variables in a neural speech-to-meaning system, compute, within an embedding space, a range of vectors of features of natural speech. Generate parameter sets for speech synthesis and synthesis speech according to the parameters. Analyze the synthesized speech to compute vectors in the embedding space. Using a cost function that favors an even spread (minimal clustering) generates a multiplicity of speech synthesis parameter sets. Using the multiplicity of parameter sets, generate a multiplicity of speech of known words that can be used as training data for speech recognition.Type: ApplicationFiled: December 5, 2019Publication date: June 10, 2021Applicant: SoundHound, Inc.Inventors: Maisy Wieman, Jonah Probell, Sudharsan Krishnaswamy