Abstract: The wait time prediction technology determines expected wait times for businesses or other public services using a model generated based on at least historical wait times for the business. In response to a request from a user, an expected wait time for service at the business for at least one particular time period on a particular day of a week is determined using the model and provided for display. User feedback regarding the expected wait time may be requested, and used to refresh the model as new wait times and other information are collected.

Abstract: A method includes inserting a set of canary text samples into a corpus of training text samples and training an external language model on the corpus of training text samples and the set of canary text samples inserted into the corpus of training text samples. For each canary text sample, the method also includes generating a corresponding synthetic speech utterance and generating an initial transcription for the corresponding synthetic speech utterance. The method also includes rescoring the initial transcription generated for each corresponding synthetic speech utterance using the external language model. The method also includes determining a word error rate (WER) of the external language model based on the rescored initial transcriptions and the canary text samples and detecting memorization of the canary text samples by the external language model based on the WER of the external language model.

Abstract: A method includes obtaining a plurality of sets of private training utterances. Each corresponding set of private training utterances is obtained from a different source and associated with a speech domain that is different than the speech domains associated with the other sets of private training utterances. The method also includes training a speech recognition model by obtaining a current version of the speech recognition model, selecting a batch of private training utterances from one of the plurality of sets of private training utterances, determining a differentially private gradient for updating the current version of the speech recognition model based on the selected batch of private training utterances, and updating the current version of the speech recognition model using the differentially private gradient. The method also includes adapting the trained speech recognition model to learn how to recognize speech in a target speech domain.

Abstract: Real-time busyness information is for a public place is computed in a privacy-sensitive way, and provided for display in relation to historical busyness information. An aggregate amount of real-time location information available for a particular public place is measured (410), and used to determine (420) whether the public place is privacy-qualified. If the public place is privacy-qualified, real-time busyness information is computed (440) for the public place based on the real-time location information. Further, it is determined (450) whether the computed real-time busyness information is accuracy-qualified, based on a comparison of the real-time busyness information to historical busyness information. If both qualifications are met, the real-time busyness information is output (470) for display or to another application.

Abstract: The wait time prediction technology determines expected wait times for businesses or other public services using a model generated based on at least historical wait times for the business. In response to a request from a user, an expected wait time for service at the business for at least one particular time period on a particular day of a week is determined using the model and provided for display. User feedback regarding the expected wait time may be requested, and used to refresh the model as new wait times and other information are collected.

Abstract: A method includes receiving a training data set including un-transcribed speech utterances that each include audio-only data not paired with any corresponding transcription, and obtaining a plurality of training canary transcriptions each including a predetermined number of words that are out-of-distribution from words of the un-transcribed speech utterances. For each training canary transcription, the method also includes generating, using TTS system, a corresponding synthetic training canary speech utterance that recites the predetermined number of words of the training canary transcription and pre-training an audio encoder on a combination of the un-transcribed speech utterances and the synthetic training canary speech utterances. The method also includes measuring an un-intended memorization of the pre-trained audio encoder based on encoder labels predicted by the pre-trained encoder for the synthetic training canary speech utterances.

Abstract: Presented are techniques of identifying, processing and displaying data point clusters associated with map information in an efficient manner. Methods and systems are disclosed which process map information to identify clusters of requested data points for display, based on iterative clustering and filtering of the data points. Methods and systems are also disclosed which generate polygons representing the clusters. The amount of data to be processed and/or displayed can be reduced, without loss of any associated information content in a displayed map.

Abstract: Presented are techniques of identifying, processing and displaying data point clusters associated with map information in an efficient manner Methods and systems are disclosed which process map information to identify clusters of requested data points for display, based on iterative clustering and filtering of the data points. Methods and systems are also disclosed which generate polygons representing the clusters. The amount of data to be processed and/or displayed can be reduced, without loss of any associated information content in a displayed map.

Abstract: Real-time busyness information is for a public place is computed in a privacy-sensitive way, and provided for display in relation to historical busyness information. An aggregate amount of real-time location information available for a particular public place is measured (410), and used to determine (420) whether the public place is privacy-qualified. If the public place is privacy-qualified, real-time busyness information is computed (440) for the public place based on the real-time location information. Further, it is determined (450) whether the computed real-time busyness information is accuracy-qualified, based on a comparison of the real-time busyness information to historical busyness information. If both qualifications are met, the real-time busyness information is output (470) for display or to another application.

Abstract: The wait time prediction technology determines expected wait times for businesses or other public services using a model generated based on at least historical wait times for the business. In response to a request from a user, an expected wait time for service at the business for at least one particular time period on a particular day of a week is determined using the model and provided for display. User feedback regarding the expected wait time may be requested, and used to refresh the model as new wait times and other information are collected.

Abstract: Presented are techniques of identifying, processing and displaying data point clusters associated with map information in an efficient manner Methods and systems are disclosed which process map information to identify clusters of requested data points for display, based on iterative clustering and filtering of the data points. Methods and systems are also disclosed which generate polygons representing the clusters. The amount of data to be processed and/or displayed can be reduced, without loss of any associated information content in a displayed map.

Abstract: Presented are techniques of identifying, processing and displaying data point clusters associated with map information in an efficient manner. Methods and systems are disclosed which process map information to identify clusters of requested data points for display, based on iterative clustering and filtering of the data points. Methods and systems are also disclosed which generate polygons representing the clusters. The amount of data to be processed and/or displayed can be reduced, without loss of any associated information content in a displayed map.

Abstract: A method includes inserting a set of canary text samples into a corpus of training text samples and training an external language model on the corpus of training text samples and the set of canary text samples inserted into the corpus of training text samples. For each canary text sample, the method also includes generating a corresponding synthetic speech utterance and generating an initial transcription for the corresponding synthetic speech utterance. The method also includes rescoring the initial transcription generated for each corresponding synthetic speech utterance using the external language model. The method also includes determining a word error rate (WER) of the external language model based on the rescored initial transcriptions and the canary text samples and detecting memorization of the canary text samples by the external language model based on the WER of the external language model.

Abstract: Real-time busyness information is for a public place is computed in a privacy-sensitive way, and provided for display in relation to historical busyness information. An aggregate amount of real-time location information available for a particular public place is measured (410), and used to determine (420) whether the public place is privacy-qualified. If the public place is privacy-qualified, real-time busyness information is computed (440) for the public place based on the real-time location information. Further, it is determined (450) whether the computed real-time busyness information is accuracy-qualified, based on a comparison of the real-time busyness information to historical busyness information. If both qualifications are met, the real-time busyness information is output (470) for display or to another application.

Abstract: Real-time busyness information is for a public place is computed in a privacy-sensitive way, and provided for display in relation to historical busyness information. An aggregate amount of real-time location information available for a particular public place is measured (410), and used to determine (420) whether the public place is privacy-qualified. If the public place is privacy-qualified, real-time busyness information is computed (440) for the public place based on the real-time location information. Further, it is determined (450) whether the computed real-time busyness information is accuracy-qualified, based on a comparison of the real-time busyness information to historical busyness information. If both qualifications are met, the real-time busyness information is output (470) for display or to another application.

Abstract: Real-time busyness information is for a public place is computed in a privacy-sensitive way, and provided for display in relation to historical busyness information. An aggregate amount of real-time location information available for a particular public place is measured (410), and used to determine (420) whether the public place is privacy-qualified. If the public place is privacy-qualified, real-time busyness information is computed (440) for the public place based on the real-time location information. Further, it is determined (450) whether the computed real-time busyness information is accuracy-qualified, based on a comparison of the real-time busyness information to historical busyness information. If both qualifications are met, the real-time busyness information is output (470) for display or to another application.

Abstract: Real-time busyness information is for a public place is computed in a privacy-sensitive way, and provided for display in relation to historical busyness information. An aggregate amount of real-time location information available for a particular public place is measured (410), and used to determine (420) whether the public place is privacy-qualified. If the public place is privacy-qualified, real-time busyness information is computed (440) for the public place based on the real-time location information. Further, it is determined (450) whether the computed real-time busyness information is accuracy-qualified, based on a comparison of the real-time busyness information to historical busyness information. If both qualifications are met, the real-time busyness information is output (470) for display or to another application.

Abstract: Presented are techniques of identifying, processing and displaying data point clusters associated with map information in an efficient manner Methods and systems are disclosed which process map information to identify clusters of requested data points for display, based on iterative clustering and filtering of the data points. Methods and systems are also disclosed which generate polygons representing the clusters. The amount of data to be processed and/or displayed can be reduced, without loss of any associated information content in a displayed map.

Abstract: Presented are techniques of identifying, processing and displaying data point clusters (850, 851) associated with map information (200) in an efficient manner. Methods and systems are disclosed which process map information (200) to identify clusters (850, 851) of requested data points for display (1020), based on iterative clustering and filtering of the data points. Methods and systems are also disclosed which generate polygons (1860, 1861, 1901-05) representing the clusters. The amount of data to be processed and/or displayed can be reduced, without loss of any associated information content in a displayed map.

Abstract: The wait time prediction technology determines expected wait times for businesses or other public services using a model generated based on at least historical wait times for the business. In response to a request from a user, an expected wait time for service at the business for at least one particular time period on a particular day of a week is determined using the model and provided for display. User feedback regarding the expected wait time may be requested, and used to refresh the model as new wait times and other information are collected.