Dialog repair based on discrepancies between user model predictions and speech recognition results
First Claim
1. A system for repairing dialog data, comprising:
- a discrepancy detection component that;
identifies discrepancy data between predictive dialog data output from a user model prediction component and recognized dialog data output from a speech recognition component, the predictive dialog data being a prediction of an action a user will pursue based on patterns of non-verbal actions by the user, and the recognized dialog data being based on a spoken command by the user;
compares the predictive dialog data and the recognized dialog data to generate difference data;
processes the difference data to determine a degree of difference between the predictive dialog data and the recognized dialog data; and
changes at least one of the predicted dialog data, the recognized dialog data, and a potential action based in part on optimization of a specific system action, the optimization being based on a utility function that comprises a chance variable, a decision variable, and a value variable; and
a dialog repair component that repairs the dialog data based in part on the discrepancy data.
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Accused Products
Abstract
An architecture is presented that leverages discrepancies between user model predictions and speech recognition results by identifying discrepancies between the predictive data and the speech recognition data and repairing the data based in part on the discrepancy. User model predictions predict what goal or action speech application users are likely to pursue based in part on past user behavior. Speech recognition results indicate what goal speech application users are likely to have spoken based in part on words spoken under specific constraints. Discrepancies between the predictive data and the speech recognition data are identified and a dialog repair is engaged for repairing these discrepancies. By engaging in repairs when there is a discrepancy between the predictive results and the speech recognition results, and utilizing feedback obtained via interaction with a user, the architecture can learn about the reliability of both user model predictions and speech recognition results for future processing.
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Citations
19 Claims
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1. A system for repairing dialog data, comprising:
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a discrepancy detection component that; identifies discrepancy data between predictive dialog data output from a user model prediction component and recognized dialog data output from a speech recognition component, the predictive dialog data being a prediction of an action a user will pursue based on patterns of non-verbal actions by the user, and the recognized dialog data being based on a spoken command by the user; compares the predictive dialog data and the recognized dialog data to generate difference data; processes the difference data to determine a degree of difference between the predictive dialog data and the recognized dialog data; and changes at least one of the predicted dialog data, the recognized dialog data, and a potential action based in part on optimization of a specific system action, the optimization being based on a utility function that comprises a chance variable, a decision variable, and a value variable; and a dialog repair component that repairs the dialog data based in part on the discrepancy data. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer-implemented method for leveraging discrepancies between user model predictions and speech recognition results for repairing dialog data, comprising:
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processing speech data into predicted dialog data and recognized dialog data, the predictive dialog data being a prediction of an action a user will pursue based on patterns of non-verbal actions by the user, and the recognized dialog data being based on a spoken command by the user; comparing the predictive dialog data and the recognized dialog data to generate difference data; processing the difference data to determine a degree of difference between the predictive dialog data and the recognized dialog data; and changing at least one of the predicted dialog data, the recognized dialog data, and a potential action based in part on optimization of a specific system action, the optimization being based on a utility function that comprises a chance variable, a decision variable, and a value variable. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A computer-implemented method for repairing dialog data in a speech application, comprising:
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identifying discrepancy data between predictive dialog data output from a predictive user model component and recognized dialog data output from a speech recognition component, the predictive dialog data being a prediction of an action a user will pursue based on patterns of non-verbal actions by the user, and the recognized dialog data being based on a spoken command by the user, and the user model component and the speech recognition component each including a plurality of modifiable parameters and structures; comparing the predictive dialog data and the recognized dialog data to generate difference data; processing the difference data to determine a degree of difference between the predictive dialog data and the recognized dialog data; and changing at least one of the predicted dialog data, the recognized dialog data, and a potential action based in part on optimization of a specific system action, the optimization being based on a utility function that comprises a chance variable, a decision variable, and a value variable. - View Dependent Claims (19)
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Specification