A system is provided for a self-learning policy engine that can be used by various spoken language understanding (SLU) processing components. The system also provides for sharing contextual information from processing performed by an upstream SLU component to a downstream SLU component to facilitate decision making by the downstream SLU component. The system also provides for a SLU component to select from a variety of actions to take. A SLU component may implement an instance of the self-learning policy that is specifically configured for the particular SLU component.

A system for voice dictation includes an earpiece, the earpiece may include an earpiece housing sized to fit into an external auditory canal of a user and block the external auditory canal, a first microphone operatively connected to the earpiece housing and positioned to be isolated from ambient sound when the earpiece housing is fitted into the external auditory canal, a second microphone operatively connected to earpiece housing and positioned to sound external from the user, and a processor disposed within the earpiece housing and operatively connected to the first microphone and the second microphone. The system may further include a software application executing on a computing device which provides for receiving the first voice audio stream into a first position of a record and receiving the second voice audio stream into a second position of the record.

Techniques are described herein for enabling an automated assistant to adjust its behavior depending on a detected age range and/or “vocabulary level” of a user who is engaging with the automated assistant. In various implementations, data indicative of a user's utterance may be used to estimate one or more of the user's age range and/or vocabulary level. The estimated age range/vocabulary level may be used to influence various aspects of a data processing pipeline employed by an automated assistant. In various implementations, aspects of the data processing pipeline that may be influenced by the user's age range/vocabulary level may include one or more of automated assistant invocation, speech-to-text (“STT”) processing, intent matching, intent resolution (or fulfillment), natural language generation, and/or text-to-speech (“TTS”) processing. In some implementations, one or more tolerance thresholds associated with one or more of these aspects, such as grammatical tolerances, vocabularic tolerances, etc., may be adjusted.

An artificial intelligence device for providing a voice recognition service includes a microphone configured to receive a voice command, a memory configured to store an error analysis model for inferring an error cause of voice recognition, an output unit, and a processor configured to determine whether voice recognition of the voice command has failed based on the voice command and voice recognition surrounding information, acquire the error cause from the voice recognition surrounding information using the error analysis model, and output the acquired error cause through the output unit.

This disclosure proposes systems and methods for processing natural language inputs using data associated with multiple language recognition contexts (LRC). A system using multiple LRCs can receive input data from a device, identify a first identifier associated with the device, and further identify second identifiers associated with the first identifier and representing candidate users of the device. The system can access language processing data used for natural language processing for the LRCs corresponding to each of the first and second identifiers, and process the input data using the language processing data at one or more stages of automatic speech recognition, natural language understanding, entity resolution, and/or command execution. User recognition can reduce the number of candidate users, and thus the amount of data used to process the input data. Dynamic arbitration can select from between competing hypotheses representing the first identifier and a second identifier, respectively.

Systems and processes for operating an intelligent automated assistant are provided. An example process includes receiving an utterance including a user request and determining whether at least a portion of the user request is ambiguous. If at least the portion of the user request is ambiguous then a set of context data based on the ambiguous portion of the user request is determined, metadata is extracted from the context data and a response to the user request is determined based on the extracted metadata.

Commentary rating determination systems and methods determine a commentary rating for commentary about a subject media content event that has been generated by a community member. An exemplary embodiment receives video information acquired by a 360° video camera, identifies a physical object from the received video information, determines a physical attribute associated with the identified physical object, wherein the determined physical attribute describes a characteristic of the identified physical object, compares the determined physical attribute of the identified physical object with a plurality of predefined physical object attributes stored in a database, and in response to identifying one of the plurality of predefined physical object attributes that matches the determined physical attribute, associates the quality value of the identified one of the plurality of predefined physical object attributes with the identified physical object. Then, the commentary rating is determined for the commentary based on the associated quality value.

In one embodiment, a method includes receiving a first user request at a client system to suspend a first task being executed by an assistant system operating on the client system, suspending the execution of the first task responsive to the first user request, receiving a second user request at the client system, determining that the second user request is a request to resume the suspended first task based on user interactions with the assistant system with respect to one or more entities associated with the first task, and presenting a prompt to resume the first task at the client system.

A content access device uses local audio translation for content presentation. The content access device receives video and first audio data associated with a first language. The content access device uses translation software and/or other automated translation services to translate the first audio data to second audio data associated with a second language. The content access device synchronizes the video with the second audio data and outputs the video and the second audio data for presentation. The first audio data may be audio, text, and so on. The second audio data may be output as audio, text, and so on.

A system for voice dictation includes an earpiece, the earpiece may include an earpiece housing sized to fit into an external auditory canal of a user and block the external auditory canal, a first microphone operatively connected to the earpiece housing and positioned to be isolated from ambient sound when the earpiece housing is fitted into the external auditory canal, a second microphone operatively connected to earpiece housing and positioned to sound external from the user, and a processor disposed within the earpiece housing and operatively connected to the first microphone and the second microphone. The system may further include a software application executing on a computing device which provides for receiving the first voice audio stream into a first position of a record and receiving the second voice audio stream into a second position of the record.