Automated meaningful phrase clustering

• US 5,860,063 A
• Filed: 07/11/1997
• Issued: 01/12/1999
• Est. Priority Date: 07/11/1997
• Status: Expired due to Term

• Current Assignee: AT&T Corporation (AT&T, Inc.)
• Original Assignee: AT&T Corporation (AT&T, Inc.)
• Inventors: Wright, Jeremy Huntley, Gorin, Allen Louis
• Primary Examiner(s): Hudspeth, David R.
• Assistant Examiner(s): Wieland, Susan
• Application Number:
  US08/893,888
• Time in Patent Office: 550 Days
• Field of Search: 704/9, 395/12, 701/231, 701/232, 701/245, 701/246, 701/243, 701/244, 701/251, 701/254, 701/255, 701/256, 701/257
• US Class Current: 704/257
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning

Abstract

A system and method for automated task selection is provided where a selected task is identified from the natural speech of the user making the selection. The system and method incorporate the selection of meaningful phrases through the use of a test for significance. The selected meaningful phrases are then clustered. The meaningful phrase clusters are input to a speech recognizer that determines whether any meaningful phrase clusters are present in the input speech. Task-type decisions are then made on the basis of the recognized meaningful phrase clusters.

Claims

1. A method for automated task classification which operates on a task objective of a user, the task objective being expressed in natural speech of the user, comprising:

selecting meaningful phrases from a plurality of transcribed speech utterances;

clustering selected ones of the meaningful phrases which occur in similar semantic contexts into meaningful phrase clusters;

detecting meaningful phrase clusters present in input speech utterances; and

making task-type classification decisions based on the detected meaningful phrase clusters in the input speech utterances.

2. The automated task classification method of claim 1, wherein the meaningful phrases are selected using a test for significance.

3. The automated task classification method of claim 1, wherein the meaningful phrases are clustered using a distortion measure between the meaningful phrases.

4. The automated task classification method of claim 3, wherein the distortion measure is based on string distortion.

5. The automated task classification method of claim 3, wherein the distortion measure is based on semantic distortion.

6. The automated task classification method of claim 3, wherein the distortion measure is a combination of string and semantic distortion.

7. The automated task classification method of claim 1, wherein the meaningful phrase clusters are detected in input speech by exactly matching the input speech to any of the meaningful phrase clusters.

8. The automated task classification method of claim 1, wherein clusters are detected in input speech by exactly or approximately matching the input speech to any of the meaningful phrase clusters.

9. The automated task classification method of claim 1, wherein the task-type classification decisions are ranked using a statistical model.

10. The automated task classification method of claim 1, further comprising a neural network for making the task-type classification decisions.

11. A method for automated call-type classification which operates on the call routing objective of a user, the call-routing objective being expressed in natural speech of the user, comprising:

selecting meaningful phrases from a plurality of transcribed speech utterances,

clustering selected ones of the meaningful phrases which occur in similar semantic contexts into meaningful phrase clusters;

detecting meaningful phrase clusters present in input speech utterances; and

making call-type classification decisions based on the detected meaningful phrase clusters in the input speech utterances.

12. The automated call-type classification method of claim 11, wherein the meaningful phrases are selected using a test for significance.

13. The automated call-type classification method of claim 11, wherein the meaningful phrases are clustered using a distortion measure between meaningful phrases.

14. The automated call-type classification method of claim 13, wherein the distortion measure is based on string distortion.

15. The automated call-type classification method of claim 13, wherein the distortion measure is based on semantic distortion.

16. The automated call-type classification method of claim 13, wherein the distortion measure is a combination of string and semantic distortion.

17. The automated call-type classification method of claim 11, wherein the meaningful phrase clusters are detected in input speech by exactly matching the input speech to any of the meaningful phrase clusters.

18. The automated call-type classification method of claim 11, wherein the meaningful phrase clusters are detected in input speech by exactly or approximately matching the input speech to any of the meaningful phrase clusters.

19. The automated call-type classification method of claim 11, wherein call-type classification decisions are ranked using a statistical model.

20. The automated call-type classification method of claim 11, wherein the call-type classification decisions are made using a neural network.

21. An automated task classification system which operates on task objectives of a user, the task objectives being expressed in natural speech of the user, comprising:

a meaningful phrase selector that selects a plurality of meaningful phrases from a set of speech utterances, each of the meaningful phrases being selected based on one of a predetermined set of the task objectives;

a meaningful phrase clustering device for clustering selected meaningful phrases which occur in similar semantic contexts into meaningful phrase clusters;

an input recognizing device for detecting any of the clustered meaningful phrases in input speech of the user, the input recognizing device having as an input the meaningful phrases clustered by the meaningful phrase clustering device; and

a classification device responsive to an input of the detected meaningful phrase clusters for making a classification decision based on the detected meaningful phrase clusters as to one of the set of predetermined task objectives.

22. The automated task classification system of claim 21 wherein the meaningful phrase selector selects meaningful phrases using a test for significance.

23. The automated task classification system of claim 21 wherein the meaningful phrase clustering device clusters meaningful phrases using a distortion measure between meaningful phrases.

24. The automated task classification system of claim 23, wherein the distortion measure is based on string distortion.

25. The automated task classification system of claim 23, wherein the distortion measure is based on semantic distortion.

26. The automated task classification system of claim 23, wherein the distortion measure is a combination of string and semantic distortion.

27. The automated task classification system of claim 21, wherein the input recognizing device detects meaningful phrase clusters in input speech by exactly matching the input speech to any of the meaningful phrase clusters.

28. The automated task classification system of claim 21, wherein the input recognizing device detects meaningful phrase clusters in input speech by exactly or approximately matching the input speech to any of the meaningful phrase clusters.

29. The automated task classification system of claim 21, wherein the classification device ranks decisions using a statistical model.

30. The automated task classification system of claim 21, wherein the classification device makes decisions using a neural network.