A speech processing system uses contextual data to determine the specific domains, subdomains, and applications appropriate for taking action in response to spoken commands and other utterances. Some applications may be given priority over others such that some applications are general request applications to which responsibility for processing an intent is to be assigned as long as contextual criteria are satisfied, while other applications are specific request applications to which responsibility for processing an intent is to be assigned only if the applications are specifically requested, if the contextual criteria of priority applications are not satisfied, and/or if certain contextual criteria associated with the specific request applications are satisfied.

Embodiments of this application provide a voice interaction method and an electronic device, and relate to the field of artificial intelligence AI technologies and the field of voice processing technologies. A specific solution includes: An electronic device may receive first voice information sent by a second user, and the electronic device recognizes the first voice information in response to the first voice information. The first voice information is used to request a voice conversation with a first user. The electronic device may have, on a basis that the electronic device recognizes that the first voice information is voice information of the second user, a voice conversation with the second user by imitating a voice of the first user and in a mode in which the first user has a voice conversation with the second user.

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

A system and method for generating a response to a user query. The method encompasses receiving, at a transceiver unit [102], the user query. The method thereafter leads to identifying, by an encoder unit [104], a user context associated with the user query based on one or more pre-stored datasets. Further the method encompasses predicting, by a prediction unit [106], one or more parameters corresponding the user query based on at least one of one or more offline-policies and one or more online-policies. The method thereafter comprises generating, by a decoder unit [108], the response to the user query based at least on the user context associated with the user query and the one or more parameters corresponding to the user query.

A method of auto-provisioning AI-based dialog services for a plurality of target applications includes storing a plurality of dialog templates, generating a deployment object associating one or more of the dialog templates with a target application from among the plurality of target applications, extracting textual data from the target application, assembling the extracted textual data into inquiries or inquiry responses according to the one or more dialog templates associated with the deployment object, and deploying an AI-based dialog service to the target application based on the assembled inquiries or inquiry responses. Each of the dialog templates may include one or more sets of common inquiries or common inquiry responses.

An example method includes receiving, by a computational assistant executing at one or more processors of a mobile computing device and via a wireless link between the mobile computing device and an external device, a representation of audio data generated by a microphone of the external device, the audio data representing a spoken utterance detected by the external device; determining, by the computational assistant and based on the audio data, a response to the spoken utterance; and sending, by the mobile computing device, to the external device, and via the wireless link between the mobile computing device and the external device, a command to output, for playback by one or more speakers connected to the external device via a hardwired analog removable connector of the external device or a wireless link between the external device and the one or more speakers, audio data representing the response to the spoken utterance.

In one embodiment, a method includes receiving at a head-mounted device a speech input from a user and a visual input captured by cameras of the head-mounted device, wherein the visual input comprises subjects and attributes associated with the subjects, and wherein the speech input comprises a co-reference to one or more of the subjects, resolving entities corresponding to the subjects associated with the co-reference based on the attributes and the co-reference, and presenting a communication content responsive to the speech input and the visual input at the head-mounted device, wherein the communication content comprises information associated with executing results of tasks corresponding to the resolved entities.

Systems and methods for human-machine interaction using a conversation system.

Systems and methods to implement commands based on selection sequences to a user interface are disclosed. Exemplary implementations may: store, electronic storage, a library of terms utterable by users that facilitate implementation of intended results; obtain audio information representing sounds captured by a client computing platform; detect the spoken terms uttered by the user present within the audio information; determine whether the spoken terms detected are included in the library of terms; responsive to determination that the spoken terms are not included in the library of terms, effectuate presentation of an error message via the user interface; record a selection sequence that the user performs subsequent to the presentation of the error message that causes a result; correlate the selection sequence with the spoken terms based on the selection sequence recorded subsequent to error message to generate correlation; and store the correlation to the electronic storage.

A computer-implemented method includes: receiving, by a computing device, text extracted from a webpage in a browser and a Uniform Resource Locator (URL) of a linked webpage associated with the text; generating, by the computing device, questions based on the text; retrieving, by the computing device, content of the linked webpage using the URL; generating, by the computing device, answers to the questions using the retrieved content; and returning, by the computing device, the questions and the answers to the browser such that the browser displays the questions and the answers in the webpage.