Method and system for building a phonotactic model for domain independent speech recognition
View Patent ↗The invention concerns a method and corresponding system for building a phonotactic model for domain independent speech recognition. The method may include recognizing phones from a user's input communication using a current phonotactic model, detecting morphemes (acoustic and/or non-acoustic) from the recognized phones, and outputting the detected morphemes for processing. The method also updates the phonotactic model with the detected morphemes and stores the new model in a database for use by the system during the next user interaction. The method may also include making task-type classification decisions based on the detected morphemes from the user's input communication.
1. A method for building a phonotactic model for domain independent speech recognition, comprising:
recognizing phones from a user's input communication using a current phonotactic model stored in a database;
detecting morphemes from the recognized phones;
creating, via a new processor, a new phonotactic model using the detected morphemes, the creating the new phonotactic model comprising transforming a prior probability distribution associated with a first domain to a prior probability distribution associated with a second domain;
replacing the current phonotactic model with the new phonotactic model in the database; and
outputting the detected morphemes for processing.
2. The method of claim 1 , wherein the morphemes include at least one of verbal speech or non-verbal speech.
3. The method of claim 2 , wherein the non-verbal speech includes 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 or touchscreen entries.
4. The method of claim 2 , wherein the user's input communication is derived from the verbal and non-verbal speech and an environment of the user.
5. The method of claim 1 , wherein the morphemes are expressed in multimodal form.
6. The method of claim 1 , wherein the morphemes in the user's input communication are derived from actions of the user, including a focus of attention of the user.
7. The method of claim 1 , further comprising:
mapping the detected acoustic morphemes into words.
8. The method of claim 1 , wherein the method operates in conjunction with one or more communication networks, the communication networks including a telephone network, the Internet, an intranet, cable TV network, a local area network (LAN), and a wireless communication network.
9. The method of claim 1 , wherein the method is used for customer care purposes.
10. The method of claim 1 , wherein the morphemes are detected using a measure of commonality within a language of the morphemes.
11. The method of claim 10 , wherein the commonality measure is a mutual information measure.
12. The method of claim 11 , wherein each of the plurality of detected morphemes has a mutual information measure exceeding a predetermined threshold.
13. The method of claim 1 , wherein detecting acoustic morphemes includes using a confidence function.
14. The method of claim 1 , wherein the new phonotactic model is used when recognizing phones for a future input communication from a user.
15. The method of claim 1 , wherein the method is iterative.
16. The method of claim 1 , wherein the morphemes are at least one of acoustic morphemes or non-acoustic morphemes.
17. The method of claim 1 , wherein creating a new phonotactic model using the detected morphemes further comprises:
mapping phone sequence statistics into word statistics; and
transforming a word probability vector of the prior probability distribution of the first domain using the mapped word statistics.
18. A method of task classification using a phonotactic model built for domain independent speech recognition, comprising:
recognizing phones from a user's input communication using a current phonotactic model stored in a database;
detecting morphemes from the recognized phones;
creating, via a processor, a new phonotactic model using the detected morpheme, the creating the new phonotactic model comprising transforming a prior probability distribution associated with a first domain to a prior probability distribution associated with a second domain;
replacing the current phonotactic model with the new phonotactic model in the database; and
making a task-type classification decision based on the detected morphemes from the user's input communication.
19. The method of claim 18 , wherein the morphemes include at least one of verbal speech or non-verbal speech.
20. The method of claim 19 , 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 or touchscreen entries.
21. The method of claim 19 , wherein the user's input communication is derived from the verbal and non-verbal speech and an environment of the user.
22. The method of claim 19 , wherein each of the verbal and non-verbal speech are directed to one of a set of predetermined task objectives and each of the verbal and non-verbal speech is labeled with one of the predetermined task objectives to which it is directed.
23. The method of claim 18 , wherein the morphemes are expressed in multimodal form.
24. The method of claim 18 , wherein the morphemes in the user's input communication are derived from actions of the user, including a focus of attention of the user.
25. The method of claim 18 , further comprising entering into a dialog with the user to obtain a feedback response from the user.
26. The method of claim 25 , wherein entering into a dialog with the user to obtain a feedback response from the user, further prompting the user to provide a feedback response including additional information with respect to an initial input communication of the user.
27. The method of claim 25 , wherein the user is prompted to provide a feedback response that includes confirmation with respect to at least one set of task objectives determined in the task-type classification decision.
28. The method of claim 18 , wherein the input communication is routed based on the task-type classification decision.
29. The method of claim 28 , wherein a task objective is performed after the input communication is routed.
30. The method of claim 18 , wherein the method operates in conjunction with one or more communication networks, the communication networks including a telephone network, the Internet, an intranet, cable TV network, a local area network (LAN), and a wireless communication network.
31. The method of claim 18 , wherein the method is used for customer care purposes.
32. The method of claim 18 , wherein the classification decisions decision and corresponding user input communications communication are collected for automated learning purposes.
33. The method of claim 18 , wherein a relationship between the detected morphemes and a predetermined set of task objectives includes a measure of usefulness of one of the morphemes to a specified one of the predetermined set of task objectives.
34. The method of claim 18 , wherein a relationship between the detected morphemes and a predetermined set of task objectives includes a measure of commonality within a language of the morphemes.
35. The method of claim 34 , wherein the commonality measure is a mutual information measure.
36. The method of claim 35 , wherein each of the detected morphemes has a mutual information measure exceeding a predetermined threshold.
37. The method of claim 18 , wherein making a task-type classification decision includes using a confidence function.
38. The method of claim 18 , wherein the input communication from the user represents a request for at least one of a set of predetermined task objectives.
39. The method of claim 18 , wherein the input communication is responsive to a query of a of form “How may I help you?”.
40. The method of claim 18 , wherein the new phonotactic model is used when recognizing phones for a future input communication from a user.
41. The method of claim 18 , wherein the method is iterative.
42. The method of claim 18 , wherein the morphemes are at least one of acoustic morphemes or non-acoustic morphemes.
43. The method of claim 18 , wherein creating a new phonotactic model using the detected morphemes further comprises:
mapping phone sequence statistics into word statistics; and
transforming a word probability vector of the prior probability distribution of the first domain using the mapped word statistics.
44. A system that builds a phonotactic model for domain independent speech recognition, comprising:
a database that stores phonotactic models;
a recognizer that recognizes phones from a user's input communication using a current phonotactic model stored in the database;
a morpheme detector that detects morphemes from the recognized phones and outputs the detected morphemes for processing; and
a learning unit that creates a new phonotactic model using the detected morphemes and transforms a prior probability distribution associated with a first domain to a prior probability distribution associated with a second domain, wherein the learning unit replaces the current phonotactic model with the new phonotactic model in the database.
45. The system of claim 44 , further comprising:
a task classification processor that makes task classification decisions based on the detected morphemes from the user's input communication.
46. The system of claim 44 , wherein the learning unit is configured to perform:
mapping phone sequence statistics into word statistics; and
transforming a word probability vector of the prior probability distribution of the first domain using the mapped word statistics.