Abstract: A system for improved natural language understanding (NLU) provides pre-feature input to a named entity recognition (NER) component. Pre-features may include no-textually derived information associated with the circumstances describing a user command (such as time, location, etc.). A domain and/or intent may also be determined prior to NER processing and may be passed to the NER component as an input. The pre-features and/or domain or intent information may assist the NER processing by providing context to a textual input, thereby improving NER processing such as semantic tagging, which in turn may improve overall NLU processing quality.

Abstract: A system for improved natural language understanding (NLU) provides pre-feature input to a named entity recognition (NER) component. Pre-features may include no-textually derived information associated with the circumstances describing a user command (such as time, location, etc.). A domain and/or intent may also be determined prior to NER processing and may be passed to the NER component as an input. The pre-features and/or domain or intent information may assist the NER processing by providing context to a textual input, thereby improving NER processing such as semantic tagging, which in turn may improve overall NLU processing quality.

Abstract: Systems, methods, and devices for training a Natural Language Understanding (NLU) component of a system using spoken utterances of individuals are described. A server sends a device, such as a speech-controlled device, a signal that causes the device to output audio soliciting content regarding how a user would speak a particular command for execution by a particular application. The device captures spoken audio and sends it to the server. The server performs speech processing on received audio data to parse the audio data into multiple portions. The server then associates a first portion of the audio data with a command indicator and a second portion of the audio data with a content indicator. The associated data is then used to update how the NLU component determines how utterances triggering the command are spoken.

Abstract: In speech processing systems, a special audio trigger indication is configured to efficiently isolate and mark incorrect speech processing results. The trigger indication may be configured to be easily recognizable by a speech processing device under various ASR and acoustic conditions. Once a speech processing device recognizes the trigger indication, incorrectly processed speech processing results are marked and may be isolated and prioritized for review by training and upgrading processes.

Abstract: Techniques are described for documenting the positions of items in a room, such as in rooms that are configured for testing automated systems that perform position-related functions. A non-contact measuring tool may be placed at different reference positions within the room. At each position, measurements are made to the room corners and to items of interest within the room. Based on this information, coordinates of the reference positions are calculated. Coordinates of the items are calculated based on the determined coordinates of the reference positions.

Abstract: In speech processing systems, a special audio trigger indication is configured to efficiently isolate and mark incorrect speech processing results. The trigger indication may be configured to be easily recognizable by a speech processing device under various ASR and acoustic conditions. Once a speech processing device recognizes the trigger indication, incorrectly processed speech processing results are marked and may be isolated and prioritized for review by training and upgrading processes.