Patents by Inventor Maria Carolina Parada San Martin
Maria Carolina Parada San Martin 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: 20240096326Abstract: A method includes receiving a spoken utterance that includes a plurality of words, and generating, using a neural network-based utterance classifier comprising a stack of multiple Long-Short Term Memory (LSTM) layers, a respective textual representation for each word of the of the plurality of words of the spoken utterance. The neural network-based utterance classifier trained on negative training examples of spoken utterances not directed toward an automated assistant server. The method further including determining, using the respective textual representation generated for each word of the plurality of words of the spoken utterance, that the spoken utterance is one of directed toward the automated assistant server or not directed toward the automated assistant server, and when the spoken utterance is directed toward the automated assistant server, generating instructions that cause the automated assistant server to generate a response to the spoken utterance.Type: ApplicationFiled: December 1, 2023Publication date: March 21, 2024Applicant: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Patent number: 11848018Abstract: A method includes receiving a spoken utterance that includes a plurality of words, and generating, using a neural network-based utterance classifier comprising a stack of multiple Long-Short Term Memory (LSTM) layers, a respective textual representation for each word of the of the plurality of words of the spoken utterance. The neural network-based utterance classifier trained on negative training examples of spoken utterances not directed toward an automated assistant server. The method further including determining, using the respective textual representation generated for each word of the plurality of words of the spoken utterance, that the spoken utterance is one of directed toward the automated assistant server or not directed toward the automated assistant server, and when the spoken utterance is directed toward the automated assistant server, generating instructions that cause the automated assistant server to generate a response to the spoken utterance.Type: GrantFiled: May 31, 2022Date of Patent: December 19, 2023Assignee: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Patent number: 11741970Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining hotword suitability. In one aspect, a method includes receiving speech data that encodes a candidate hotword spoken by a user, evaluating the speech data or a transcription of the candidate hotword, using one or more predetermined criteria, generating a hotword suitability score for the candidate hotword based on evaluating the speech data or a transcription of the candidate hotword, using one or more predetermined criteria, and providing a representation of the hotword suitability score for display to the user.Type: GrantFiled: January 6, 2022Date of Patent: August 29, 2023Assignee: Google LLCInventors: Andrew E. Rubin, Johan Schalkwyk, Maria Carolina Parada San Martin
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Patent number: 11676625Abstract: A method for training an endpointer model includes short-form speech utterances and long-form speech utterances. The method also includes providing a short-form speech utterance as input to a shared neural network, the shared neural network configured to learn shared hidden representations suitable for both voice activity detection (VAD) and end-of-query (EOQ) detection. The method also includes generating, using a VAD classifier, a sequence of predicted VAD labels and determining a VAD loss by comparing the sequence of predicted VAD labels to a corresponding sequence of reference VAD labels. The method also includes, generating, using an EOQ classifier, a sequence of predicted EOQ labels and determining an EOQ loss by comparing the sequence of predicted EOQ labels to a corresponding sequence of reference EOQ labels. The method also includes training, using a cross-entropy criterion, the endpointer model based on the VAD loss and the EOQ loss.Type: GrantFiled: January 20, 2021Date of Patent: June 13, 2023Assignee: Google LLCInventors: Shuo-Yiin Chang, Bo Li, Gabor Simko, Maria Carolina Parada San Martin, Sean Matthew Shannon
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Patent number: 11551709Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting an end of a query are disclosed. In one aspect, a method includes the actions of receiving audio data that corresponds to an utterance spoken by a user. The actions further include applying, to the audio data, an end of query model. The actions further include determining the confidence score that reflects a likelihood that the utterance is a complete utterance. The actions further include comparing the confidence score that reflects the likelihood that the utterance is a complete utterance to a confidence score threshold. The actions further include determining whether the utterance is likely complete or likely incomplete. The actions further include providing, for output, an instruction to (i) maintain a microphone that is receiving the utterance in an active state or (ii) deactivate the microphone that is receiving the utterance.Type: GrantFiled: January 31, 2020Date of Patent: January 10, 2023Assignee: Google LLCInventors: Gabor Simko, Maria Carolina Parada San Martin, Sean Matthew Shannon
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Patent number: 11545147Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for classification using neural networks. One method includes receiving audio data corresponding to an utterance. Obtaining a transcription of the utterance. Generating a representation of the audio data. Generating a representation of the transcription of the utterance. Providing (i) the representation of the audio data and (ii) the representation of the transcription of the utterance to a classifier that, based on a given representation of the audio data and a given representation of the transcription of the utterance, is trained to output an indication of whether the utterance associated with the given representation is likely directed to an automated assistance or is likely not directed to an automated assistant.Type: GrantFiled: May 2, 2019Date of Patent: January 3, 2023Assignee: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Publication number: 20220293101Abstract: A method includes receiving a spoken utterance that includes a plurality of words, and generating, using a neural network-based utterance classifier comprising a stack of multiple Long-Short Term Memory (LSTM) layers, a respective textual representation for each word of the of the plurality of words of the spoken utterance. The neural network-based utterance classifier trained on negative training examples of spoken utterances not directed toward an automated assistant server. The method further including determining, using the respective textual representation generated for each word of the plurality of words of the spoken utterance, that the spoken utterance is one of directed toward the automated assistant server or not directed toward the automated assistant server, and when the spoken utterance is directed toward the automated assistant server, generating instructions that cause the automated assistant server to generate a response to the spoken utterance.Type: ApplicationFiled: May 31, 2022Publication date: September 15, 2022Applicant: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Patent number: 11361768Abstract: A method includes receiving a spoken utterance that includes a plurality of words, and generating, using a neural network-based utterance classifier comprising a stack of multiple Long-Short Term Memory (LSTM) layers, a respective textual representation for each word of the of the plurality of words of the spoken utterance. The neural network-based utterance classifier trained on negative training examples of spoken utterances not directed toward an automated assistant server. The method further including determining, using the respective textual representation generated for each word of the plurality of words of the spoken utterance, that the spoken utterance is one of directed toward the automated assistant server or not directed toward the automated assistant server, and when the spoken utterance is directed toward the automated assistant server, generating instructions that cause the automated assistant server to generate a response to the spoken utterance.Type: GrantFiled: July 21, 2020Date of Patent: June 14, 2022Assignee: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Publication number: 20220130399Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining hotword suitability. In one aspect, a method includes receiving speech data that encodes a candidate hotword spoken by a user, evaluating the speech data or a transcription of the candidate hotword, using one or more predetermined criteria, generating a hotword suitability score for the candidate hotword based on evaluating the speech data or a transcription of the candidate hotword, using one or more predetermined criteria, and providing a representation of the hotword suitability score for display to the user.Type: ApplicationFiled: January 6, 2022Publication date: April 28, 2022Applicant: Google LLCInventors: Andrew E. Rubin, Johan Schalkwyk, Maria Carolina Parada San Martin
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Patent number: 10929754Abstract: A method for training an endpointer model includes short-form speech utterances and long-form speech utterances. The method also includes providing a short-form speech utterance as input to a shared neural network, the shared neural network configured to learn shared hidden representations suitable for both voice activity detection (VAD) and end-of-query (EOQ) detection. The method also includes generating, using a VAD classifier, a sequence of predicted VAD labels and determining a VAD loss by comparing the sequence of predicted VAD labels to a corresponding sequence of reference VAD labels. The method also includes, generating, using an EOQ classifier, a sequence of predicted EOQ labels and determining an EOQ loss by comparing the sequence of predicted EOQ labels to a corresponding sequence of reference EOQ labels. The method also includes training, using a cross-entropy criterion, the endpointer model based on the VAD loss and the EOQ loss.Type: GrantFiled: December 11, 2019Date of Patent: February 23, 2021Assignee: Google LLCInventors: Shuo-yiin Chang, Bo Li, Gabor Simko, Maria Carolina Parada San Martin, Sean Matthew Shannon
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Publication number: 20200349946Abstract: A method includes receiving a spoken utterance that includes a plurality of words, and generating, using a neural network-based utterance classifier comprising a stack of multiple Long-Short Term Memory (LSTM) layers, a respective textual representation for each word of the of the plurality of words of the spoken utterance. The neural network-based utterance classifier trained on negative training examples of spoken utterances not directed toward an automated assistant server. The method further including determining, using the respective textual representation generated for each word of the plurality of words of the spoken utterance, that the spoken utterance is one of directed toward the automated assistant server or not directed toward the automated assistant server, and when the spoken utterance is directed toward the automated assistant server, generating instructions that cause the automated assistant server to generate a response to the spoken utterance.Type: ApplicationFiled: July 21, 2020Publication date: November 5, 2020Applicant: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Patent number: 10762894Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for keyword spotting. One of the methods includes training, by a keyword detection system, a convolutional neural network for keyword detection by providing a two-dimensional set of input values to the convolutional neural network, the input values including a first dimension in time and a second dimension in frequency, and performing convolutional multiplication on the two-dimensional set of input values for a filter using a frequency stride greater than one to generate a feature map.Type: GrantFiled: July 22, 2015Date of Patent: September 1, 2020Assignee: GOOGLE LLCInventors: Tara N. Sainath, Maria Carolina Parada San Martin
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Patent number: 10714096Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining hotword suitability. In one aspect, a method includes receiving speech data that encodes a candidate hotword spoken by a user, evaluating the speech data or a transcription of the candidate hotword, using one or more predetermined criteria, generating a hotword suitability score for the candidate hotword based on evaluating the speech data or a transcription of the candidate hotword, using one or more predetermined criteria, and providing a representation of the hotword suitability score for display to the user.Type: GrantFiled: May 16, 2018Date of Patent: July 14, 2020Assignee: Google LLCInventors: Andrew Rubin, Johan Schalkwyk, Maria Carolina Parada San Martin
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Publication number: 20200168242Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting an end of a query are disclosed. In one aspect, a method includes the actions of receiving audio data that corresponds to an utterance spoken by a user. The actions further include applying, to the audio data, an end of query model. The actions further include determining the confidence score that reflects a likelihood that the utterance is a complete utterance. The actions further include comparing the confidence score that reflects the likelihood that the utterance is a complete utterance to a confidence score threshold. The actions further include determining whether the utterance is likely complete or likely incomplete. The actions further include providing, for output, an instruction to (i) maintain a microphone that is receiving the utterance in an active state or (ii) deactivate the microphone that is receiving the utterance.Type: ApplicationFiled: January 31, 2020Publication date: May 28, 2020Applicant: Google LLCInventors: Gabor Simko, Maria Carolina Parada San Martin, Sean Matthew Shannon
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Publication number: 20200117996Abstract: A method for training an endpointer model includes short-form speech utterances and long-form speech utterances. The method also includes providing a short-form speech utterance as input to a shared neural network, the shared neural network configured to learn shared hidden representations suitable for both voice activity detection (VAD) and end-of-query (EOQ) detection. The method also includes generating, using a VAD classifier, a sequence of predicted VAD labels and determining a VAD loss by comparing the sequence of predicted VAD labels to a corresponding sequence of reference VAD labels. The method also includes, generating, using an EOQ classifier, a sequence of predicted EOQ labels and determining an EOQ loss by comparing the sequence of predicted EOQ labels to a corresponding sequence of reference EOQ labels. The method also includes training, using a cross-entropy criterion, the endpointer model based on the VAD loss and the EOQ loss.Type: ApplicationFiled: December 11, 2019Publication date: April 16, 2020Applicant: Google LLCInventors: Shuo-yiin Chang, Bo Li, Gabor Simko, Maria Carolina Parada San Martin, Sean Matthew Shannon
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Patent number: 10593352Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting an end of a query are disclosed. In one aspect, a method includes the actions of receiving audio data that corresponds to an utterance spoken by a user. The actions further include applying, to the audio data, an end of query model. The actions further include determining the confidence score that reflects a likelihood that the utterance is a complete utterance. The actions further include comparing the confidence score that reflects the likelihood that the utterance is a complete utterance to a confidence score threshold. The actions further include determining whether the utterance is likely complete or likely incomplete. The actions further include providing, for output, an instruction to (i) maintain a microphone that is receiving the utterance in an active state or (ii) deactivate the microphone that is receiving the utterance.Type: GrantFiled: June 6, 2018Date of Patent: March 17, 2020Assignee: Google LLCInventors: Gabor Simko, Maria Carolina Parada San Martin, Sean Matthew Shannon
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Publication number: 20200051551Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for keyword spotting. One of the methods includes training, by a keyword detection system, a convolutional neural network for keyword detection by providing a two-dimensional set of input values to the convolutional neural network, the input values including a first dimension in time and a second dimension in frequency, and performing convolutional multiplication on the two-dimensional set of input values for a filter using a frequency stride greater than one to generate a feature map.Type: ApplicationFiled: October 16, 2019Publication date: February 13, 2020Applicant: Google LLCInventors: Tara N. Sainath, Maria Carolina Parada San Martin
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Publication number: 20190304459Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for classification using neural networks. One method includes receiving audio data corresponding to an utterance. Obtaining a transcription of the utterance. Generating a representation of the audio data. Generating a representation of the transcription of the utterance. Providing (i) the representation of the audio data and (ii) the representation of the transcription of the utterance to a classifier that, based on a given representation of the audio data and a given representation of the transcription of the utterance, is trained to output an indication of whether the utterance associated with the given representation is likely directed to an automated assistance or is likely not directed to an automated assistant.Type: ApplicationFiled: May 2, 2019Publication date: October 3, 2019Inventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Patent number: 10311872Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for classification using neural networks. One method includes receiving audio data corresponding to an utterance. Obtaining a transcription of the utterance. Generating a representation of the audio data. Generating a representation of the transcription of the utterance. Providing (i) the representation of the audio data and (ii) the representation of the transcription of the utterance to a classifier that, based on a given representation of the audio data and a given representation of the transcription of the utterance, is trained to output an indication of whether the utterance associated with the given representation is likely directed to an automated assistance or is likely not directed to an automated assistant.Type: GrantFiled: July 25, 2017Date of Patent: June 4, 2019Assignee: Google LLCInventors: Nathan David Howard, Gabor Simko, Maria Carolina Parada San Martin, Ramkarthik Kalyanasundaram, Guru Prakash Arumugam, Srinivas Vasudevan
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Patent number: 10229700Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting voice activity. In one aspect, a method include actions of receiving, by a neural network included in an automated voice activity detection system, a raw audio waveform, processing, by the neural network, the raw audio waveform to determine whether the audio waveform includes speech, and provide, by the neural network, a classification of the raw audio waveform indicating whether the raw audio waveform includes speech.Type: GrantFiled: January 4, 2016Date of Patent: March 12, 2019Assignee: Google LLCInventors: Tara N. Sainath, Gabor Simko, Maria Carolina Parada San Martin, Ruben Zazo Candil