METHOD AND SYSTEM FOR AUTOMATIC DETECTING MORPHEMES IN A TASK CLASSIFICATION SYSTEM USING LATTICES

• US 20080177544A1
• Filed: 09/13/2007
• Published: 07/24/2008
• Est. Priority Date: 11/05/1999
• Status: Abandoned Application

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: AT&T Corporation (AT&T, Inc.)
• Inventors: Riccardi, Giuseppe, Wright, Jeremy Huntley, Gorin, Allen Louis, Petrovska-Delacretaz, Dijana
• Application Number:
  US11/854,717
• Publication Number:
  US 20080177544A1
• Time in Patent Office: Days
• US Class Current: 704/254
• CPC Class Codes: G10L 15/08 Speech classification or search

Abstract

The invention concerns a method and system for detecting morphemes in a user'"'"'s communication. The method may include recognizing a lattice of phone strings from the user'"'"'s input communication, the lattice representing a distribution over the phone strings, and detecting morphemes in the user'"'"'s input communication using the lattice. The morphemes may be acoustic and/or non-acoustic. The morphemes may represent any unit or sub-unit of communication including phones, diphones, phone-phrases, syllables, grammars, words, gestures, tablet strokes, body movements, mouse clicks, etc. The training speech may be verbal, non-verbal, a combination of verbal and non-verbal, or multimodal.

Claims

1. A method of detecting morphemes using lattices in an automated task classification system which operates on one or more task objectives of a user, comprising:

recognizing a lattice of phone strings representing morphemes in the user'"'"'s input communication, the lattice representing a distribution over the phone strings;

detecting morphemes in the user'"'"'s input communication using the recognized lattice; and

making task-type classification decisions based on the detected morphemes in the user'"'"'s input communication.

2. The automated task classification method of claim 1, wherein the morphemes include at least one of verbal speech and non-verbal speech.

3. The automated task classification method of claim 2, wherein the non-verbal speech includes the use of at least one of gestures, body movements, head movements, non-responses, text, keyboard entries, keypad entries, mouse clicks, DTMF codes, pointers, stylus, cable set-top box entries, graphical user interface entries and touchscreen entries.

4. The automated task classification method of claim 1, wherein the morphemes are expressed in multimodal form.

5. The automated task classification method of claim 1, wherein the user'"'"'s input communication is derived from the verbal and non-verbal speech and the user'"'"'s environment.

6. The automated task classification method of claim 1, wherein the morphemes in the user'"'"'s input communication are derived from the user'"'"'s actions, including the user'"'"'s focus of attention.

7. The automated task classification method of claim 1, further comprising entering into a dialog with the user to obtain a feedback response from the user.

8. The automated task classification method of claim 1, wherein the user is prompted to provide a feedback response includes additional information with respect to the user'"'"'s initial input communication.

9. The automated task classification method of claim 1, wherein the user is prompted to provide a feedback response that includes confirmation with respect to at least one of the set of task objectives determined in the classification decision.

10. The automated task classification method of claim 1, wherein the input communication is routed based on the classification decision.

11. The automated task classification method of claim 1, wherein the task objective is performed after the user'"'"'s input communication is routed.

12. The automated task classification method of claim 1, wherein the method operates in conjunction with one or more communication networks, the one or more communication networks including at least one of a telephone network, the Internet, an intranet, Cable TV network, a local area network (LAN), and a wireless communication network.

13. The automated task classification method of claim 1, wherein the method is used for customer care purposes.

14. The automated task classification method of claim 1, wherein the classification decisions and corresponding user input communications are collected for automated learning purposes.

15. The automated task classification method of claim 1, wherein the relationship between the generated morphemes and the one or more task objectives includes a measure of usefulness of a one of the morphemes to a specified one of the predetermined task objectives.

16. The automated task classification method of claim 15, wherein the usefulness measure is a salience measure.

17. The automated task classification method of claim 16, wherein the salience measure is represented as a conditional probability of the task objective being requested given an appearance of the morpheme in the input communication, the conditional probability being a highest value in a distribution of the conditional probabilities over the set of predetermined task objectives.

18. The automated task classification method of claim 16, wherein each of the plurality of generated morphemes has a salience measure exceeding a predetermined threshold.

19. The automated task classification method of claim 1, wherein the relationship between the generated morphemes and the predetermined set of task objectives includes a measure of commonality within a language of the morphemes.

20. The automated task classification method of claim 19, wherein the commonality measure is a mutual information measure.

21. The automated task classification method of claim 20, wherein each of the plurality of generated morphemes has a mutual information measure exceeding a predetermined threshold.

22. The automated task classification method of claim 1, wherein the input communication from the user represents a request for at least one of the set of predetermined task objectives.

23. The automated task classification method of claim 1, wherein the input communication is responsive to a query of a form “

How may I help you?”

.

24. The automated task classification method of claim 2, wherein each of the verbal and non-verbal speech are directed to one of the one or more task objectives and each of the verbal and non-verbal speech is labeled with the one task objective to which it is directed.

25. The automated task classification method of claim 1, wherein the morphemes are generated by clustering selected ones of salient sub-morphemes from training speech which are semantically and syntactically similar, stored in a database which is used to detect morphemes in the recognized lattice.