A display apparatus includes an input unit configured to receive a user command; an output unit configured to output a registration suitability determination result for the user command; and a processor configured to generate phonetic symbols for the user command, analyze the generated phonetic symbols to determine registration suitability for the user command, and control the output unit to output the registration suitability determination result for the user command. Therefore, the display apparatus may register a user command which is resistant to misrecognition and guarantees high recognition rate among user commands defined by a user.

Disclosed herein are systems, computer-implemented methods, and computer-readable media for dialog modeling. The method includes receiving spoken dialogs annotated to indicate dialog acts and task/subtask information, parsing the spoken dialogs with a hierarchical, parse-based dialog model which operates incrementally from left to right and which only analyzes a preceding dialog context to generate parsed spoken dialogs, and constructing a functional task structure of the parsed spoken dialogs. The method can further either interpret user utterances with the functional task structure of the parsed spoken dialogs or plan system responses to user utterances with the functional task structure of the parsed spoken dialogs. The parse-based dialog model can be a shift-reduce model, a start-complete model, or a connection path model.

It is an aspect of the present disclosure to provide a dialogue system capable of providing an extended function to the user by registering a new vocabulary that matches the user's preference and by changing the pre-stored conversation pattern.

An information processing apparatus includes an acquisition unit that acquires voice data and image data, respectively, a display control unit that performs control to display the image data acquired by the acquisition unit in synchronization with the voice data, a reception unit that receives a display element to be added for display to a specific character in the image data displayed by the display control unit, and a setting unit that sets a playback period in which the specific character in the voice data is played back, as a display period of the display element received by the reception unit in the image data.

The technology of this application relates to a response method in a human-computer dialogue, a dialogue system, and a storage medium, and belongs to the field of artificial intelligence. In a process of a dialogue between a user and a machine, a user intent of a current dialogue is determined based on an expected user intent associated with a sentence replied by the machine to the user in a previous dialogue, so that a response is made. Because processing logic for an expected user intent is introduced, accuracy of a generated response sentence is improved.

Methods, systems, and apparatus, for defining and monitoring an event for a physical entity and the performance of an action in response to the occurrence of the event. A method includes receiving data indicating an event for a physical entity, the event specified in part by a physical environment feature for which the occurrence of the event is to be monitored by the data processing apparatus; receiving data indicating an action associated with the event and to be taken in response to the occurrence of the event; monitoring for the occurrence of the event for the physical entity; and in response to the occurrence of the event, causing the action associated with the event to be performed.

Text independent speaker recognition models can be utilized by an automated assistant to verify a particular user spoke a spoken utterance and/or to identify the user who spoke a spoken utterance. Implementations can include automatically updating a speaker embedding for a particular user based on previous utterances by the particular user. Additionally or alternatively, implementations can include verifying a particular user spoke a spoken utterance using output generated by both a text independent speaker recognition model as well as a text dependent speaker recognition model. Furthermore, implementations can additionally or alternatively include prefetching content for several users associated with a spoken utterance prior to determining which user spoke the spoken utterance.

Dialog system training techniques using a simulated user system are described. In one example, a simulated user system supports multiple agents. The dialog system, for instance, may be configured for use with an application (e.g., digital image editing application). The simulated user system may therefore simulate user actions involving both the application and the dialog system which may be used to train the dialog system. Additionally, the simulated user system is not limited to simulation of user interactions by a single input mode (e.g., natural language inputs), but also supports multimodal inputs. Further, the simulated user system may also support use of multiple goals within a single dialog session

A method includes receiving, at an electronic device, a command directed to a first application operated by the electronic device. Features presented by the first application in response to interactions with the first application are captured at the electronic device. Data communicated with the first application via the interactions with the first application are captured at the electronic device. A task is learned based on the captured features and communicated data.

A method for training a classification device, the method comprising: receiving classification device constraints at an intermediary device; receiving training data at the intermediary device; matching the training data to the classification device constraints to provide constrained training data; mapping the constrained training data to classification device functionality to provide a command model; and transmitting the command model to the classification device.