Method and apparatus for creation and user-customization of speech-enabled services

• US 20020072914A1
• Filed: 12/08/2000
• Published: 06/13/2002
• Est. Priority Date: 12/08/2000
• Status: Abandoned Application

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Douglas, Shona, Alshawi, Hiyan
• Application Number:
  US09/732,600
• Publication Number:
  US 20020072914A1
• Time in Patent Office: Days
• US Class Current: 704/270.1
• CPC Class Codes: G06F 40/45 Example-based machine translation; Alignment
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 2015/223 Execution procedure of a spoken command
• CPC Class Codes: H04M 2201/40 using speech recognition speech recognition per se G10L15/00
• CPC Class Codes: H04M 3/4936 Speech interaction details speech recognition per se G10L15/00

Abstract

Embodiments of the invention relate to a system and method for providing speech-enabled application programs. The speech-enabled programs automatically execute requests input by users. One or more natural language variants may be mapped with at least one natural language exemplar. The natural language exemplar may correspond to a typical way to express a request relevant to the speech-enabled application program. The natural language variant may correspond to an alternative way of expressing the same request. A recognized input string is received and a prospective variant that most resembles the received recognized input string is selected from the natural language variants. The natural language exemplar mapped to the prospective variant is identified. An action instruction associated with the identified natural language exemplar is executed to fulfill the user'"'"'s request.

Claims

1. A method for providing speech-enabled application programs comprising:

responsive to an input string, selecting from one or more natural language variants a prospective variant that most resembles the input string; and

identifying a natural language exemplar via a mapping between the exemplar and the prospective variant.

2. The method of claim 1, wherein the mapping comprises:

mapping the one or more natural language variants with at least one natural language exemplar.

3. The method of claim 2, wherein the prospective variant corresponds to at least one natural language exemplar.

4. The method of claim 1, further comprising:

executing an action instruction associated with the identified natural language exemplar.

5. The method of claim 1, further comprising:

mapping a plurality of action instructions with a plurality of natural language exemplars, wherein each action instruction is associated with at least one natural language exemplar.

6. The method of claim 5, further comprising:

generating a mapping function that specifies a difference between the input string and the prospective variant.

7. The method of claim 6, further comprising:

applying the mapping function to the action instruction associated with the identified natural language exemplar to produce an adapted action instruction.

8. The method of claim 7, further comprising:

executing the produced adapted action instruction.

9. The method of claim 6, further comprising:

applying the mapping function to the identified natural language exemplar to produce an adapted exemplar.

10. The method of claim 9, further comprising

forwarding the adapted exemplar to a user to confirm whether the user desires an adapted action corresponding to the adapted exemplar.

11. The method of claim 10, further comprising:

executing the adapted action if the user confirms that an adapted exemplar expresses the action desired by the user.

12. The method of claim 11, further comprising:

if the user does not accept that the adapted exemplar expresses the action desired by the user, selecting from the one or more natural language variants an alternative prospective variant that most resembles the input string; and

identifying a natural language exemplar via a mapping between the exemplar and the alternative prospective variant.

13. The method of claim 12, further comprising:

executing an action instruction associated with the identified natural language exemplar.

14. The method of claim 2, further comprising:

storing one or more natural language variants mapped to at least one natural language exemplar in a memory.

15. The method of claim 14, wherein at least one natural language variant is input by a user.

16. The method of claim 14, wherein at least one natural language variant is input by an application developer.

17. The method of claim 14, wherein the at leas t one natural language exemplar is input by an application developer.

18. The method of claim 14, wherein the at least one natural language exemplar is produced automatically by a natural language generator.

19. The method of claim 14, further comprising:

producing at least one natural language variant by automatically generating paraphrases of the natural language exemplar.

20. The method of claim 1, further comprising:

loading an active context file relating to a service accessed by a user, the active context file containing the one or more natural language variants and the natural language exemplar.

21. The method of claim 1, further comprising:

comparing the input string with the one or more natural language variants.

22. The method of claim 1, wherein the input string is input by at least one of a keyboard, handwriting recognition device, a dial pad, and a speech recognition device.

24. The system of claim 23, further comprising:

a controller adapted to execute an action instruction associated with the identified natural language exemplar corresponding to the prospective variant.

25. The system of claim 23, the processor adapted to map a plurality of action instructions with a plurality of natural language exemplars, wherein each action instruction is associated with at least one natural language exemplar and the memory to store the mapped action instructions.

26. The system of claim 25, the processor adapted to further generate a mapping function that specifies a difference between the received recognized input string and the prospective variant.

27. The system of claim 26, the processor adapted to apply the mapping function to the action instruction associated with the identified natural language exemplar mapped to the prospective variant to produce an adapted action instruction.

28. The system of claim 27, the controller adapted to execute the produced adapted action instruction.

29. The system of claim 28, further comprising:

an output synthesizer to present a result of the executed instruction by providing data that can be presented to an audio or visual terminal device.

30. The system of claim 29, wherein the output synthesizer is at least one of a display format and a speech synthesizer.

31. The system of claim 23, further comprising:

an input device to generate an input string.

32. The system of claim 31, wherein said input device is at least one of a keyboard, handwriting recognition device, a dial pad, and a speech recognition device.

23. A system for providing speech-enabled application programs comprising:

a voice recognizer to receive an input string and produce a recognized input string;

a memory to store one or more natural language variants corresponding to at least one natural language exemplar; and

a processor to;

select from the one or more natural language variants a prospective variant that most resembles the received recognized input string; and

identify the at least one natural language exemplar corresponding to the prospective variant.

33. A machine-readable medium having stored thereon executable instructions for performing a method comprising:

responsive to an input string, selecting from one or more natural language variants a prospective variant that most resembles the input string; and

identifying a natural language exemplar via a mapping between the exemplar and the prospective variant.

34. The machine-readable medium of claim 33 having stored thereon further executable instructions for performing a method comprising:

mapping the one or more natural language variants with at least one natural language exemplar.

35. The machine-readable medium of claim 33 having stored thereon further executable instructions for performing a method comprising:

executing an action instruction associated with the identified natural language exemplar.

36. The machine-readable medium of claim 33 having stored thereon further executable instructions for performing a method comprising:

mapping a plurality of action instructions with a plurality of natural language exemplars, wherein each action instruction is associated with at least one natural language exemplar.

37. The machine-readable medium of claim 36 having stored thereon further executable instructions for performing a method comprising:

generating a mapping function that specifies a difference between the input string and the prospective variant.

38. The machine-readable medium of claim 37 having stored thereon further executable instructions for performing a method comprising:

applying the mapping function to the action instruction associated with the identified natural language exemplar to produce an adapted action instruction.

39. The machine-readable medium of claim 38 having stored thereon further executable instructions for performing a method comprising:

executing the produced adapted action instruction.

40. The machine-readable medium of claim 37 having stored thereon further executable instructions for performing a method comprising:

applying the mapping function to the identified natural language exemplar to produce an adapted exemplar.

41. The machine-readable medium of claim 40 having stored thereon further executable instructions for performing a method comprising:

forwarding the adapted exemplar to a user to confirm whether the user desires an adapted action corresponding to the adapted exemplar.

42. The machine-readable medium of claim 41 having stored thereon further executable instructions for performing a method comprising:

executing the adapted action if the user confirms that an adapted exemplar expresses the action desired by the user.

43. The machine-readable medium of claim 42 having stored thereon further executable instructions for performing a method comprising:

selecting from the one or more natural language variants an alternative prospective variant that most resembles the input string, if the user does not accept that the adapted exemplar expresses the action desired by the user; and

identifying a natural language exemplar via a mapping between the exemplar and the alternative prospective variant.

44. The machine-readable medium of claim 43 having stored thereon further executable instructions for performing a method comprising:

executing an action instruction associated with the identified natural language exemplar.

46. The method of claim 45, wherein the created variant represents one preferred way of expressing the desired action.

47. The method of claim 46, further comprising:

storing the created variant in a customized context file, wherein during service access by a user the personalized context file is uploaded by the speech-enabled service allowing the user to express the desired action using the created variant.

48. The method of claim 45, wherein the context file is accessed using a web browser.

49. The method of claim 45, wherein the context file is accessed using a telephone.

45. In a speech-enabled service, a method for creating customized files containing personalized command variants relating to the speech-enabled service, the method comprising:

accessing a context file relating to the speech enabled service, the context file containing a natural language exemplar associated with a desired action;

creating a customized variant for the desired action; and

correlating the created variant with the natural language exemplar.

50. A system for providing speech-enabled application programs comprising:

a memory to store one or more natural language variants corresponding to a natural language exemplar; and

a processor to;

select from the one or more natural language variants a prospective variant that most resembles an input string; and

identify a natural language exemplar via a mapping between the exemplar and the prospective variant.

51. The system of claim 50, further comprising:

a voice recognizer to receive the input string and produce a recognized input string.

52. The system of claim 50, further comprising:

a controller adapted to execute an action instruction associated with the identified natural language exemplar.

53. The system of claim 50, the processor adapted to map the one or more natural language variants with the natural language exemplar.

54. The system of claim 50, the processor adapted to map a plurality of action instructions with a plurality of natural language exemplars, wherein each action instruction is associated with at least one natural language exemplar and the memory to store the mapped action instructions.

55. The system of claim 51, the processor adapted to generate a mapping function that specifies a difference between the recognized input string and the prospective variant.

56. The system of claim 55, the processor adapted to apply the mapping function to an action instruction associated with the identified natural language exemplar to produce an adapted action instruction.

57. The system of claim 56, further comprising:

a controller adapted to execute the produced adapted action instruction.

58. The system of claim 57, further comprising:

an output synthesizer to present a result of the executed instruction by providing data that can be presented to an audio or visual terminal device.

59. The system of claim 58, wherein the output synthesizer is at least one of a display format and a speech synthesizer.

60. The system of claim 50, further comprising:

an input device to generate the input string.

61. The system of claim 60, wherein said input device is at least one of a keyboard, handwriting recognition device, a dial pad, and a speech recognition device.