Spoken dialog system using a best-fit language model and best-fit grammar

1. A spoken dialog system using a best-fit language model, comprising:

a dialog manager, coupled to a language model selector, that provides to the language model selector a current dialog state;

the language model selector, coupled to a plurality of dialog-state dependent language models, that selects one of the plurality of dialog-state dependent language models;

the plurality of dialog-state dependent language models that are interpolated from a general-task language model;

a large vocabulary continuous speech recognizer, coupled to the dialog manager and the language model selector, that receives input speech and generates a first hypothesis result for the input speech with a likelihood score, based on the selected dialog-state dependent language model;

the general-task language model, coupled to the large vocabulary continuous speech recognizer, that enables the large vocabulary continuous speech recognizer to generate a second hypothesis with a second likelihood score; and

a plurality of dialog strategies based on the language model system, coupled to the dialog manager.

2. The spoken dialog system of claim 1 , wherein the language model selector selects one of the plurality of dialog-state dependent language models based on the current dialog state.

3. The spoken dialog system of claim 1 , wherein an end result is chosen from the higher value of the first likelihood score and the second likelihood score.

4. The spoken dialog system of claim 1 , wherein the dialog manager deploys a plurality of various dialog components to improve dialog performance.

5. A spoken dialog system using best-fit grammar, comprising:

a dialog manager, coupled to a grammar selector, that provides to the grammar selector a dialog state, the dialog state being dependent upon a user's utterances;

the grammar selector, coupled to a plurality of dialog-state dependent sub-grammars, that selects one of the plurality of dialog-state dependent sub-grammars;

the plurality of dialog-state dependent sub-grammars, that contain a plurality of speech patterns;

a speech recognition module, coupled to the dialog manager and the grammar selector, that receives input speech;

a general-purpose grammar, coupled to the grammar selector, that contains patterns of general user responses; and

a plurality of dialog strategies based on the selected grammar system, coupled to the dialog manager, to enhance dialog performance.

6. The spoken dialog system of claim 5 , wherein the grammar selector chooses one of the plurality of dialog-state dependent sub-grammars based on the current dialog state.

7. The spoken dialog system of claim 5 , wherein the dialog manager commands the grammar selector to use the general-purpose grammar, if the selected dialog-state dependent sub-grammar fails to provide a matching pattern to the input speech.

8. The spoken dialog system of claim 5 , wherein each of the plurality of dialog-state dependent sub-grammars is specific to at least one of a defined sub-task.

9. The spoken dialog system of claim 5 , wherein a speech understanding module, coupled to the dialog manager and the grammar selector, receives a word sequence generated by the speech recognition module.

10. The spoken dialog system of claim 5 , wherein the dialog manager deploys a plurality of various dialog components to improve dialog performance.

11. A spoken dialog system implementing a best-fit language model, comprising a computer readable medium and a computer readable program code stored on the computer readable medium having instructions to:

receive a current dialog state from a dialog manager;

select a dialog-state dependent language model from a plurality of dialog-state dependent language models based on the current dialog state;

generate a first hypothesis result for input speech with a first likelihood score;

generate a second hypothesis result for input speech with a second likelihood score;

select a best-fit language model from the higher value of the first likelihood score and the second likelihood score; and

implement dialog strategies, based on the best-fit language model, to improve dialog performance.

12. The spoken dialog system of claim 11 , wherein the instructions are provided to a language model selector to select the dialog-state dependent language model.

13. The system of claim 12 , wherein the instructions are provided to a large vocabulary continuous speech recognizer to receive input speech and generate the first hypothesis result based on the selected dialog-state dependent language model.

14. The system of claim 13 , wherein the instructions are provided to the large vocabulary continuous speech recognizer from a general-task language model to generate the second hypothesis result.

15. The system of claim 11 , wherein the instructions are provided to the dialog manager to implement at least one of a plurality of dialog strategies to further improve accuracy and enhance dialog performance.

16. A spoken dialog system implementing best-fit grammar, comprising a computer readable medium and a computer readable program code stored on the computer readable medium having instructions to:

receive a dialog state from a dialog manager, the dialog state being dependent upon a user's utterances;

select one of a plurality of dialog-state dependent sub-grammars based on the current dialog state;

select a general-purpose grammar, if the chosen dialog-state dependent sub-grammar fails to provide a matching pattern of input speech; and

implement dialog strategies, based on one of the dialog-state dependent sub-grammar and the general-purpose grammar, to improve dialog performance.

17. The spoken dialog system of claim 16 , wherein the instructions are provided to a grammar selector to select one of the plurality of dialog-state dependent sub-grammars.

18. The spoken dialog system of claim 16 , wherein the instructions are provided from the dialog manager to the grammar selector to select the general-purpose grammar that contains patterns of general user responses.

19. The spoken dialog system of claim 16 , wherein each of the plurality of dialog-state dependent sub-grammars is specific to at least one defined task.

20. The system of claim 16 , wherein the instructions are provided to the dialog manager to implement at least one of a plurality of dialog strategies to further improve accuracy and enhance dialog performance.

21. A method of implementing a best-fit language model in a spoken dialog system, comprising:

receiving a current dialog state from a dialog manager;

choosing a dialog-state dependent language model from a plurality of dialog-state dependent language models based on the current dialog state;

calculating a first hypothesis result for input speech with a first likelihood score;

calculating a second hypothesis result for input speech with a second likelihood score;

choosing a best-fit language model from the higher value of the first likelihood score and the second likelihood score; and

deploying dialog strategies, based on the best-fit language model, to improve dialog performance.

22. The method of claim 21 , wherein a language model selector chooses the dialog-state dependent language model.

23. The method of claim 21 , wherein a large vocabulary continuous speech recognizer receives input speech and calculates the first hypothesis result based on the selected dialog-state dependent language model and calculates the second hypothesis result from input speech from a general-task language model.

24. The method of claim 23 , wherein an end result is selected from the greater value of the first likelihood score and the second likelihood score.

25. A method of implementing a best-fit grammar model in a spoken dialog system, comprising:

receiving a current dialog state from a dialog manager, the current dialog state being dependent upon a user's utterances;

choosing one of a plurality of dialog-state dependent sub-grammars based on the current dialog state;

choosing a general-purpose grammar, if the chosen dialog-state dependent sub-grammar fails to provide a matching pattern of input speech; and

employing dialog strategies, based on one of the dialog-state dependent sub-grammar and the general-purpose grammar, to improve dialog performance.

26. The method of claim 25 , wherein a grammar selector chooses one of the plurality of dialog-state dependent sub-grammars.

27. The method of claim 25 , wherein the grammar selector chooses the general-purpose grammar that contains patterns of general speech.

28. The method of claim 25 , wherein the dialog manager employs at least one of a plurality of dialog strategies to further enhance dialog performance and improve recognition accuracy.

29. A spoken dialog system using a best-fit language model and best-fit grammar, comprising:

a dialog manager, coupled to a language model selector and a grammar selector, that provides to the language model selector and to the grammar selector a current dialog state;

the language model selector, coupled to a plurality of dialog-state dependent language models, that selects one of the plurality of dialog-state dependent language models;

the grammar selector, coupled to a plurality of dialog-state dependent sub-grammars, that contain a plurality of speech patterns;

the plurality of dialog-state dependent language models that are interpolated from a general-task language model;

the plurality of dialog-state dependent sub-grammars that contain a plurality of speech patterns;

a large vocabulary continuous speech recognizer, coupled to the dialog manager and the language model selector and a language understanding module, that receives input speech and generates a first hypothesis result for the input speech with a likelihood score, based on the selected dialog-state dependent language model;

the language understanding module, coupled to the dialog manager and to the grammar selector and to large vocabulary continuous speech recognizer, that extracts critical information from a word sequence generated by the large vocabulary continuous speech recognizer;

the general-task language model, coupled to the large vocabulary continuous speech recognizer, that enables the large vocabulary continuous speech recognizer to generate a second hypothesis with a second likelihood score;

a general-purpose grammar, coupled to the grammar selector, that contains patterns of general user responses; and

a plurality of dialog strategies based on the language model system and a plurality of dialog strategies based on the selected grammar system, coupled to the dialog manager.

30. The spoken dialog system of claim 29 , wherein an end result is chosen from the higher value of the first likelihood score and the second likelihood score.

31. The spoken dialog system of claim 29 , wherein the dialog manager commands the grammar selector to use the general-purpose grammar, if the selected dialog-state dependent sub-grammar fails to provide a matching pattern to the input speech.

32. A spoken dialog system implementing a best-fit language model and best-fit grammar, comprising a computer readable medium and a computer readable program code stored on the computer readable medium having instructions to:

receive a current dialog state from a dialog manager;

select a dialog-state dependent language model from a plurality of dialog-state dependent language models based on the current dialog state;

select a dialog-state dependent sub-grammar from a plurality of dialog-state dependent sub-grammars based on the current dialog state;

select a general-purpose grammar, if the chosen dialog-state dependent sub-grammar fails to provide a matching pattern of input speech;

generate a first hypothesis result for input speech with a first likelihood score;

generate a second hypothesis result for input speech with a second likelihood score;

select a best-fit language model from the higher value of the first likelihood score and the second likelihood score; and

implement dialog strategies, based on the best-fit language model and based on one of the dialog-state dependent sub-grammar and the general-purpose grammar.

33. The spoken dialog system of claim 32 , wherein the instructions are provided to a language model selector to select the dialog-state dependent language model.

34. The system of claim 33 , wherein the instructions are provided to a large vocabulary continuous speech recognizer to receive input speech and generate the first hypothesis result based on the selected dialog-state dependent language model.

35. The system of claim 34 , wherein the instructions are provided to the large vocabulary continuous speech recognizer from a general-task language model to generate the second hypothesis result.

36. The system of claim 32 , wherein the instructions are provided to the dialog manager to implement at least one of a plurality of dialog strategies to further improve accuracy and enhance dialog performance.

37. The spoken dialog system of claim 32 , wherein the instructions are provided to a grammar selector to select one of the plurality of dialog-state dependent sub-grammars.

38. The spoken dialog system of claim 32 , wherein the instructions are provided from the dialog manager to the grammar selector to select the general-purpose grammar that contains patterns of general user responses.

39. The spoken dialog system of claim 32 , wherein each of the plurality of dialog-state dependent sub-grammars is specific to at least one defined task.