Interpretation of user commands is accelerated through digital user interfaces of various modalities, including generation and presentation of command modifications for rapid correction of incomplete or erroneous user commands. An embodiment detects whether the interpreted command is accurate and, if inaccurate, precisely what was the intended command or, at least, what suggested modification to the interpreted command would be sufficient to match the intent of the user. Disambiguation occurs that entails multiple recommendation generators proposing modified commands that may more accurately reflect the intent of the user. The user may provide a response that is either a confirmation of which one of several modified commands that were automatically proposed does the user intend or a correction that computer device may use to filter or replace currently offered modified commands to generate improved modified commands. By iterative refinement, the user and computer device may quickly agree on an accurate command that the computer device should execute.

Disclosed herein is a tempo setting device including a detecting unit that deems a predetermined utterance as a detection target and detects the utterance of the detection target through recognizing sound, a tempo deciding unit that decides a tempo based on a detection interval of the detected utterance in response to two or more times of consecutive detection of the utterance of the detection target by the detecting unit, and a setting unit that sets the tempo decided by the tempo deciding unit.

Disclosed herein is a tempo setting device including a detecting unit that deems a predetermined utterance as a detection target and detects the utterance of the detection target through recognizing sound, a tempo deciding unit that decides a tempo based on a detection interval of the detected utterance in response to two or more times of consecutive detection of the utterance of the detection target by the detecting unit, and a setting unit that sets the tempo decided by the tempo deciding unit.

State-of-satisfaction change pattern models each including a set of transition weights in state sequences of the states of satisfaction are obtained for predetermined change patterns of the states of satisfaction, and a state-of-satisfaction estimation model for obtaining the posteriori probability of the utterance feature amount given the state of satisfaction of an utterer is obtained by using the utterance-for-learning feature amount and a correct value of the state of satisfaction of an utterer who gave an utterance for learning corresponding to the utterance-for-learning feature amount. By using the input utterance feature amount and the state-of-satisfaction change pattern models and the state-of-satisfaction estimation model, an estimated value of the state of satisfaction of an utterer who gave an utterance corresponding to the input utterance feature amount is obtained.

State-of-satisfaction change pattern models each including a set of transition weights in state sequences of the states of satisfaction are obtained for predetermined change patterns of the states of satisfaction, and a state-of-satisfaction estimation model for obtaining the posteriori probability of the utterance feature amount given the state of satisfaction of an utterer is obtained by using the utterance-for-learning feature amount and a correct value of the state of satisfaction of an utterer who gave an utterance for learning corresponding to the utterance-for-learning feature amount. By using the input utterance feature amount and the state-of-satisfaction change pattern models and the state-of-satisfaction estimation model, an estimated value of the state of satisfaction of an utterer who gave an utterance corresponding to the input utterance feature amount is obtained.

A set of candidate intent vectors is generated from an input intent vector. A validation of the set of candidate intent vectors is performed that selects as valid intent vectors any of the set of candidate intent vectors that are semantically similar to the input intent vector.

A set of candidate intent vectors is generated from an input intent vector. A validation of the set of candidate intent vectors is performed that selects as valid intent vectors any of the set of candidate intent vectors that are semantically similar to the input intent vector.

A voice recognition system for an elevator system including: one or more microphones configured to capture a voice command from an individual and convert the voice command into an audio signal; a command arbitrator including one or more speech interpretation systems, the command arbitrator being configured to analyze the audio signal and determine an interpreted command for the elevator system from the audio signal using the one or more speech interpretation systems, wherein the interpreted command includes a confidence measure associated with the interpreted command, and wherein the confidence measure is an indicator depicting how confident the command arbitrator is that the interpreted command matches the voice command from the individual.

A voice recognition system for an elevator system including: one or more microphones configured to capture a voice command from an individual and convert the voice command into an audio signal; a command arbitrator including one or more speech interpretation systems, the command arbitrator being configured to analyze the audio signal and determine an interpreted command for the elevator system from the audio signal using the one or more speech interpretation systems, wherein the interpreted command includes a confidence measure associated with the interpreted command, and wherein the confidence measure is an indicator depicting how confident the command arbitrator is that the interpreted command matches the voice command from the individual.

A processor may identify an anomaly in one or more communications. A processor may monitor the one or more communications for an utterance. A processor may perform natural language processing (NLP) on the utterance. A processor may generate an understanding of the utterance using natural language understanding (NLU). A processor may detect the anomaly from the understanding of the utterance. A processor may execute a response, responsive to detecting the anomaly.