Abstract: Techniques are provided for using a virtual assistant to optimize multi-step processes to enhance a user's ability and efficiency in performing tasks. In one particular aspect, a computer-implemented method is provided that includes obtaining input data from one or more cameras of a head-mounted device, detecting, from the input data, objects and relationships between the objects for performing a task, generating a symbolic task state based on the objects and the relationships between the objects, feeding, using a domain specific planning language, the symbolic task state and a corresponding desired task goal state into a planner, generating, using the planner, a plan that includes a sequence of actions to perform the task and achieve the corresponding desired task goal, and in response to executing the sequence of actions in the plan, rendering, on a display of the head-mounted device, virtual content in an extended reality environment.

Abstract: Disclosed in various examples are methods, systems, and machine-readable mediums for providing improved computer implemented speech recognition by detecting and correcting speech recognition errors during a speech session. The system recognizes repeated speech commands from a user in a speech session that are similar or identical to each other. To correct these repeated errors, the system creates a customized language model that is then utilized by the language modeler to produce a refined prediction of the meaning of the repeated speech commands. The custom language model may comprise clusters of similar past predictions of speech commands from the speech session of the user.

Abstract: Disclosed in various examples are methods, systems, and machine-readable mediums for providing improved computer implemented speech recognition by detecting and correcting speech recognition errors during a speech session. The system recognizes repeated speech commands from a user in a speech session that are similar or identical to each other. To correct these repeated errors, the system creates a customized language model that is then utilized by the language modeler to produce a refined prediction of the meaning of the repeated speech commands. The custom language model may comprise clusters of similar past predictions of speech commands from the speech session of the user.