Abstract: Techniques for enrolling a user in a system's user recognition functionality without requiring the user speak particular speech are described. The system may determine characteristics unique to a user input. The system may generate an implicit voice profile from user inputs having similar characteristics. After an implicit voice profile is generated, the system may receive a user input having speech characteristics similar to that of the implicit voice profile. The system may ask the user if the user wants the system to associate the implicit voice profile with a particular user identifier. If the user responds affirmatively, the system may request an identifier of a user profile (e.g., a user name). In response to receiving the user's name, the system may identify a user profile associated with the name and associate the implicit voice profile with the user profile, thereby converting the implicit voice profile into an explicit voice profile.

Abstract: A computerized personal assistant includes a natural language user interface, a natural language processing machine, an identity machine, and a knowledge-base updating machine. The knowledge-base updating machine is configured to update a user-centric artificial intelligence knowledge base associated with the particular user to include a new or updated user-centric fact based on the computer-readable representation of the user input, wherein the knowledge-base updating machine updates the user-centric artificial intelligence knowledge base via an update protocol useable by a plurality of different computer services.

Abstract: A first neural network model of a user device processes audio data to extract audio embeddings that represent vocal characteristics of a user of an utterance represented in the audio data. The audio embeddings may then be hashed to remove characteristics specific to the user while still maintaining a unique set of characteristics. The hashed embeddings may be sent to a remote system, which may use them to identify the user.

Abstract: Techniques for labeling user inputs for updating user recognition voice profiles are described. A system may leverage various signals, generated during or after processing of a user input, to retroactively determine which user spoke the user input. For example, after the system receives the user input, the user may provide the system with non-spoken user verification information. Based on such user verification information, the system may label the previously spoken user input as originating from the particular user. The system may also or alternatively use system usage history to retroactively label user inputs.

Abstract: Techniques for updating voice profiles used to perform user recognition are described. A system may use clustering techniques to update voice profiles. When the system receives audio data representing a spoken user input, the system may store the audio data. Periodically, the system may recall, from storage, audio data (representing previous user inputs). The system may identify clusters of the audio data, with each cluster including similar or identical speech characteristics. The system may determine a cluster is substantially similar to an existing voice profile. If this occurs, the system may create an updated voice profile using the original voice profile and the cluster of audio data.

Abstract: A first neural network model of a user device processes audio data to extract audio embeddings that represent vocal characteristics of a user of an utterance represented in the audio data. The audio embeddings may then be hashed to remove characteristics specific to the user while still maintaining a unique set of characteristics. The hashed embeddings may be sent to a remote system, which may use them to identify the user.

Abstract: Techniques for updating voice profiles used to perform user recognition are described. A system may use clustering techniques to update voice profiles. When the system receives audio data representing a spoken user input, the system may store the audio data. Periodically, the system may recall, from storage, audio data (representing previous user inputs). The system may identify clusters of the audio data, with each cluster including similar or identical speech characteristics. The system may determine a cluster is substantially similar to an existing voice profile. If this occurs, the system may create an updated voice profile using the original voice profile and the cluster of audio data.

Abstract: Systems, methods, and devices related to establishing voice identity profiles for use with voice-controlled devices are provided. The embodiments disclosed enhance user experience by customizing the enrollment process to utilize voice recognition for each user based on historical information which can be used in the selection process of phrases a user speaks during enrollment of a voice recognition function or skill. The selection process can utilize phrases that have already been spoken to the electronic device; it can utilize phrases, contacts, or other personalized information it can obtain from the user account of the person enrolling; it can use any of the information just described to select specific words to enhance the probably of achieving higher phonetic matches based on words the individual user is more likely to speak to the device.

Abstract: A computerized personal assistant includes a natural language user interface, a natural language processing machine, an identity machine, and a knowledge-base updating machine. The knowledge-base updating machine is configured to update a user-centric artificial intelligence knowledge base associated with the particular user to include a new or updated user-centric fact based on the computer-readable representation of the user input, wherein the knowledge-base updating machine updates the user-centric artificial intelligence knowledge base via an update protocol useable by a plurality of different computer services.

Abstract: Systems and methods for locating the end of a keyword in voice sensing are provided. An example method includes receiving an acoustic signal that includes a keyword portion immediately followed by a query portion. The acoustic signal represents at least one captured sound. The method further includes determining the end of the keyword portion. The method further includes, separating, using the end of the keyword portion, the query portion from the keyword portion of the acoustic signal. The method further includes providing the query portion, absent any part of the keyword portion, to an automatic speech recognition (ASR) system.

Abstract: Systems and methods for keyword detection using keyword repetitions are provided. An example method includes receiving an acoustic signal representing at least one captured sound. Using a keyword model, a first confidence score for the first acoustic signal may be acquired. The method also includes determining the first confidence score is less than a detection threshold within a first value. In response, lowering the threshold by a second value for a pre-determined time interval. The method also includes receiving a second acoustic signal captured during the pre-determined time interval and acquiring a second confidence score for the second acoustic signal. The method also includes determining the second confidence score equals or exceeds the lowered threshold, and then confirming keyword detection. The threshold may be restored after the pre-determined time interval. The keyword model may be temporarily replaced by a tuned keyword model to facilitate keyword detection in low SNR conditions.

Abstract: Self-cooled engine including a cylinder, a cylinder head and a turbo-piston which freely reciprocates inside the cylinder. The cylinder head has a valve that achieves circumferential suction of air-fuel mixture into the cylinder. The valve mechanism is closed and opened by cylindrical cam by means of cam shaft. Circumferential suction of air-fuel mixture enables the cylinder to cool itself and to burn the fuel at the energy center effectively. The force of incoming stream of air-fuel mixture rotates the impeller on the piston which acts as a fan to cool the cylinder walls. The impeller blades deflect the flame from reaching the cylinder walls and acts as a thermal barrier between the energy center and cylinder walls. The high intensity compression swirl (HICS) created at the end of the compression stroke to ensure that the fuel combustion is efficient and instantaneous release of maximum energy.

Abstract: A method for diagnosing problems with protection of a data source and recovery of the same. The method includes diagnosing a copy of data located at the storage location and diagnosing a temporal version of the copy of data. Based on the diagnosis it is determined whether an error or a warning was detected. If either were detected a response is provided with a suggested solution.

Abstract: Self-cooled engine including a cylinder, a cylinder head and a turbo-piston which freely reciprocates inside the cylinder. The cylinder head has a valve that achieves circumferential suction of air-fuel mixture into the cylinder. The valve mechanism is closed and opened by cylindrical cam by means of cam shaft. Circumferential suction of air-fuel mixture enables the cylinder to cool itself and to burn the fuel at the energy center effectively. The force of incoming stream of air-fuel mixture rotates the impeller on the piston which acts as a fan to cool the cylinder walls. The impeller blades deflect the flame from reaching the cylinder walls and acts as a thermal barrier between the energy center and cylinder walls. The high intensity compression swirl (HICS) created at the end of the compression stroke to ensure that the fuel combustion is efficient and instantaneous release of maximum energy.

Abstract: A method for diagnosing problems with protection of a data source and recovery of the same. The method includes diagnosing a copy of data located at the storage location and diagnosing a temporal version of the copy of data. Based on the diagnosis it is determined whether an error or a warning was detected. If either were detected a response is provided with a suggested solution.

Abstract: A system and method is provided for automatically generating an interactive voice response (IVR) application flow from recorded audio files of calls to or from an IVR system. The method includes identifying an IVR state sequence for each of a plurality of calls from recorded audio files of the calls. The N most common state sequences are identified for the IVR system for the plurality of calls. An application flow is then displayed for the IVR application using the N most common state sequences.

Abstract: A method for diagnosing problems with protection of a data source and recovery of the same. The method includes diagnosing a copy of data located at the storage location and diagnosing a temporal version of the copy of data. Based on the diagnosis it is determined whether an error or a warning was detected. If either were detected a response is provided with a suggested solution.

Abstract: A method for diagnosing problems with protection of a data source and recovery of the same. The method includes diagnosing a copy of data located at the storage location and diagnosing a temporal version of the copy of data. Based on the diagnosis it is determined whether an error or a warning was detected. If either were detected a response is provided with a suggested solution.

Abstract: A method for diagnosing problems with protection of a data source and recovery of the same. The method includes diagnosing a copy of data located at the storage location and diagnosing a temporal version of the copy of data. Based on the diagnosis it is determined whether an error or a warning was detected. If either were detected a response is provided with a suggested solution.

Abstract: A method for diagnosing problems with protection of a data source and recovery of the same. The method includes diagnosing a copy of data located at the storage location and diagnosing a temporal version of the copy of data. Based on the diagnosis it is determined whether an error or a warning was detected. If either were detected a response is provided with a suggested solution.