Speaker-dependent dialog adaptation

• US 20060206333A1
• Filed: 06/29/2005
• Published: 09/14/2006
• Est. Priority Date: 03/08/2005
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Paek, Timothy S., Chickering, David M., Horvitz, Eric J.
• Application Number:
  US11/170,998
• Publication Number:
  US 20060206333A1
• Time in Patent Office: Days
• US Class Current: 704/260
• CPC Class Codes: G10L 15/07 to the speaker
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue

Abstract

A simulation environment for adapting a speech model (e.g., baseline model) to a user is provided. The user can interact with a base parametric speech model (e.g., statistical model with learnable parameters such as a Bayesian network) and give positive and/or negative feedback when the dialog system has performed what the user considers to be appropriate and/or inappropriate action(s). From the user feedback, the dialog system learns to take actions customized for the particular user. Speaker-dependent adaptation can be extended to the dialog level by performing maximum likelihood linear regression (MLLR) adaptation simultaneously with dialog personalization. Users are immediately able to observe how their feedback has caused the dialog system to adapt, and can quit training whenever they feel that the dialog system has adapted enough for current purposes.

Claims

1. A simulation environment facilitating adaptation of a speech model to a user comprising:

an user interface component that provides an utterance for the user to utter; and

,

a dialog system that comprises;

a speech model having a plurality of modifiable parameters, the speech model receives the utterance from the user and recognizes the utterance; and

,

a utility model that modifies the parameters of the speech model based upon feedback associated with a response to the recognized utterance and a utility of action(s), action sequence(s) and/or action type(s).

2. The environment of claim 1, employed repeatedly to adapt the speech model to the user.

3. The environment of claim 1 with maximum likelihood linear regression performed in order to modify the parameters on the parameters of the speech model from the data gathered from the environment.

4. A speech model trained by the simulation environment of claim 1.

5. The environment of claim 1, further comprising a language model that specifies the utterances associated with a particular domain, the utterance provided by the user interface component based on the utterances specified by the language model.

6. The environment of claim 1, the user interface component further simulates a noisy environment with respect to the utterance received by the dialog system.

7. The environment of claim 1, the utility model comprising an influence diagram.

8. The environment of claim 1, the utility model employs local distributions that are decision trees.

9. The environment of claim 1, the feedback depends on a design associated with the user interface component.

10. The environment of claim 1, the learning component further modifies the parameters of the speech model based upon the utterance received from the adaptation component.

11. A method of adapting a speech model to a user comprising:

receiving an utterance from the user;

recognizing the utterance using a speech model having modifiable parameters;

responding to the recognized utterance;

receiving feedback from the user regarding appropriateness of the response;

adjusting a utility model based on the feedback; and

,

adjusting parameters of the speech model based on the feedback.

12. The method of claim 11 further comprising:

receiving information regarding the utterance;

adjusting parameters of the speech model based on the utterance and the recognize utterance.

13. The method of claim 11 performed iteratively in order to adapt the speech model to the user.

14. The method of claim 13, each iteration based on a different utterance, the utterances based on a language model that comprises utterances associated with a particular domain.

15. The method of claim 11, further comprising simultaneously simulating a noisy environment when the utterance is received from the user.

16. A computer readable medium having stored thereon computer executable instructions for carrying out the method of claim 11.

17. A computer readable medium having stored thereon computer executable instructions for the speech model adapted by the method of claim 11.

18. A simulation environment that facilitates adaptation of a speech model to a user comprising:

means for providing an utterance for a user to utter;

means for recognizing the utterance;

means for adjusting parameters of the means for recognizing the utterance based upon feedback associated with a response to the recognize utterance; and

,

means for further adjusting parameters of the means for recognizing the utterance based upon maximum likelihood linear regression.

19. The simulation environment of claim 18, performed iteratively during a training session, each iteration based on a different utterance.

20. The simulation environment of claim 19, the utterances based on a language model that comprises utterances associated with a particular domain.