Abstract: A computing system is described that predicts a future action to be taken by a user of a computing device and determines, based on contextual information associated with the computing device, a current action being taken by the user. The computing system determines, based on the current action, a degree of likelihood of whether the user will be able to take the future action and predicts, based on the degree of likelihood, that the user will not be able to take the future action. The computing system sends information to the computing device indicating that the current action being taken by the user will lead to the user not being able to take the future action.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for hotword detection on multiple devices are disclosed. In one aspect, a method includes the actions of receiving audio data that corresponds to an utterance. The actions further include determining that the utterance likely includes a particular, predefined hotword. The actions further include transmitting (i) data indicating that the computing device likely received the particular, predefined hotword, (ii) data identifying the computing device, and (iii) data identifying a group of nearby computing devices that includes the computing device. The actions further include receiving an instruction to commence speech recognition processing on the audio data. The actions further include in response to receiving the instruction to commence speech recognition processing on the audio data, processing at least a portion of the audio data using an automated speech recognizer on the computing device.

Abstract: A computer-implemented technique can include detecting, by a first computing device, a set of user communications at least one of transmitted to and received by from a second computing device via a first communication mode, identifying a second communication mode that is available for communication between the first and second computing devices, and obtaining an appropriateness score for the first and second communication modes based on a contextual feature of the set of user communications, wherein the contextual feature relates an appropriateness of a particular communication mode for the set of user communications, and wherein each appropriateness score is indicative of a level of the appropriateness of a particular communication mode for the set of user communications. The technique can also include selectively outputting a suggestion to switch from the first communication mode to the second communication mode.

Abstract: A system is described that, after executing a parameterless search query to identify results that are determined to be relevant to a user of a device, determines a respective target property, for each of the results, that indicates whether a result is associated with audio content, visual content, audio and visual content, haptic content, or textual content. The system also determines one or more dynamic properties, of the device, that indicate one or more of a network connection strength, connection status to an audio output device, battery level, or connection status to a multimedia streaming device. Based on the one or more dynamic properties of the computing device and the respective target property of each of the results, the system determines a dynamic ranking of the results and transmits the results and the dynamic ranking to the device.

Abstract: In some implementations, data indicating a touch received on a proximity-sensitive display is received while the proximity-sensitive display is presenting one or more items. In one aspect, the techniques describe may involve a process for disambiguating touch selections of hypothesized items, such as text or graphical objects that have been generated based on input data, on a proximity-sensitive display. This process may allow a user to more easily select hypothesized items that the user may wish to correct, by determining whether a touch received through the proximity-sensitive display represents a selection of each hypothesized item based at least on a level of confidence that the hypothesized item accurately represents the input data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for natural language processing. One of the method includes receiving a voice input from a user device; generating a recognition output; receiving a user selection of one or more terms in the recognition output; receiving a user input of one or more letters replacing the user selected one or more terms; determining suggested correction candidates based in part on the user input and the voice input; and providing one or more suggested correction candidates to the user device as suggested corrected recognition outputs.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for natural language processing One of the methods includes receiving a first voice input from a user device, generating a first recognition output, receiving a user selection of one or more terms in the first recognition output- receiving a second voice input spelling a correction of the user selection, determining a corrected recognition output for the selected portion; and providing a second recognition output that merges the first recognition output and the corrected recognition output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for hotword detection on multiple devices are disclosed. In one aspect, a method includes the actions of receiving, by a computing device, audio data that corresponds to an utterance. The actions further include determining a likelihood that the utterance includes a hotword. The actions further include determining a loudness score for the audio data. The actions further include based on the loudness score, determining an amount of delay time. The actions further include, after the amount of delay time has elapsed, transmitting a signal that indicates that the computing device will initiate speech recognition processing on the audio data.

Abstract: A method includes receiving a received message and a draft reply message to the received message, the received message including a received message component, the received message component representing one or more of a question, a request, and a subject. The method also includes parsing the received message to detect the received message component and parsing the draft reply message into one or more reply message components, identifying, using one or more machine learning models, informational components associated with the received message component and the one or more reply message components by at least applying the machine learning module to the received message component and each reply message component of the one or more reply message, and identifying, based on the one or more informational components, one or more deficient components in the subject draft reply message, and outputting, for display, information about the one or more deficient components.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving audio data corresponding to an utterance, determining that the audio data corresponds to a hotword, generating a hotword audio fingerprint of the audio data that is determined to correspond to the hotword, comparing the hotword audio fingerprint to one or more stored audio fingerprints of audio data that was previously determined to correspond to the hotword, detecting whether the hotword audio fingerprint matches a stored audio fingerprint of audio data that was previously determined to correspond to the hotword based on whether the comparison indicates a similarity between the hotword audio fingerprint and one of the one or more stored audio fingerprints that satisfies a predetermined threshold, and in response to detecting that the hotword audio fingerprint matches a stored audio fingerprint, disabling access to a computing device into which the utterance was spoken.

Abstract: A computing system is described that predicts a future action to be taken by a user of a computing device and determines, based on contextual information associated with the computing device, a current action being taken by the user. The computing system determines, based on the current action, a degree of likelihood of whether the user will be able to take the future action and predicts, based on the degree of likelihood, that the user will not be able to take the future action. The computing system sends information to the computing device indicating that the current action being taken by the user will lead to the user not being able to take the future action.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for natural language processing. One of the method includes receiving a voice input from a user device; generating a recognition output; receiving a user selection of one or more terms in the recognition output; receiving a user input of one or more letters replacing the user selected one or more terms; determining suggested correction candidates based in part on the user input and the voice input; and providing one or more suggested correction candidates to the user device as suggested corrected recognition outputs.

Abstract: In some implementations, data indicating a touch received on a proximity-sensitive display is received while the proximity-sensitive display is presenting one or more items. In one aspect, the techniques describe may involve a process for disambiguating touch selections of hypothesized items, such as text or graphical objects that have been generated based on input data, on a proximity-sensitive display. This process may allow a user to more easily select hypothesized items that the user may wish to correct, by determining whether a touch received through the proximity-sensitive display represents a selection of each hypothesized item based at least on a level of confidence that the hypothesized item accurately represents the input data.

Abstract: In general, the subject matter can be embodied in methods, systems, and program products for using a display of a computing device as a light source. The computing device receives a first image that was captured by a camera of the computing device, determines a brightness of at least part of the first image, selects an intensity of a light source that is to be presented by the display of the computing device, the intensity of the light source being selected based on the determined brightness of the at least part of the image, presents the light source by the display of the computing device, the presented light source having the selected intensity, and receives a second image that is captured by the camera of the computing device while the computing device is presenting the light source on the display using the selected intensity.

Abstract: In some implementations, a language proficiency of a user of a client device is determined by one or more computers. The one or more computers then determines a text segment for output by a text-to-speech module based on the determined language proficiency of the user. After determining the text segment for output, the one or more computers generates audio data including a synthesized utterance of the text segment. The audio data including the synthesized utterance of the text segment is then provided to the client device for output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for natural language processing. One of the method includes receiving a voice input from a user device; generating a recognition output; receiving a user selection of one or more terms in the recognition output; receiving a user input of one or more letters replacing the user selected one or more terms; determining suggested correction candidates based in part on the user input and the voice input; and providing one or more suggested correction candidates to the user device as suggested corrected recognition outputs.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for hotword detection on multiple devices are disclosed. In one aspect, a method includes the actions of receiving, by a computing device, audio data that corresponds to an utterance. The actions further include determining a likelihood that the utterance includes a hotword. The actions further include determining a loudness score for the audio data. The actions further include based on the loudness score, determining an amount of delay time. The actions further include, after the amount of delay time has elapsed, transmitting a signal that indicates that the computing device will initiate speech recognition processing on the audio data.

Abstract: A computer-executed technique for grouping and summarizing messages in a graphical user interface (GUI) of a communication application includes received messages and determining a plurality of communication topics therein. The messages are clustered into groups based on the communication topics such that messages in each group relate to the same topic. The messages are displayed in the GUI in the determined groups. A user can select a group to ungroup and view the messages. The communication topics upon which the clustering is based may be specific to each user in the communication session.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating audio output samples predicted to be communicated by a user. One example system includes a first user device having a first user. The first user device initiates a communication session between the first user and a second user of a second user device. The first user device obtains a neural network model of the second user. The neural network model is trained to generate, conditioned on audio input samples received up to a current time step, an audio output sample predicted to be communicated by the second user at a next time step. The user device repeatedly provides received audio input samples as input to the neural network model and plays audio output samples generated by the neural network model in place of received audio input samples communicated by the second user.

Abstract: In general, the subject matter can be embodied in methods, systems, and program products for using a display of a computing device as a light source. The computing device receives a first image that was captured by a camera of the computing device, determines a brightness of at least part of the first image, selects an intensity of a light source that is to be presented by the display of the computing device, the intensity of the light source being selected based on the determined brightness of the at least part of the image, presents the light source by the display of the computing device, the presented light source having the selected intensity, and receives a second image that is captured by the camera of the computing device while the computing device is presenting the light source on the display using the selected intensity.