Non-enrolled continuous dictation

• US 20080004876A1
• Filed: 06/30/2006
• Published: 01/03/2008
• Est. Priority Date: 06/30/2006
• Status: Abandoned Application

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Duchnowski, Paul, He, Chuang, Wu, Jianxiong, Deshmukh, Neeraj
• Application Number:
  US11/478,837
• Publication Number:
  US 20080004876A1
• Time in Patent Office: Days
• US Class Current: 704/243
• CPC Class Codes: G10L 15/065 Adaptation
• CPC Class Codes: G10L 15/144 Training of HMMs

Abstract

Speech recognition includes use of a user profile for large vocabulary continuous speech recognition which is created without using an enrollment procedure. The user profile includes speech recognition information associated with a specific user. Large vocabulary continuous speech recognition is performed on an unknown speech input from the user utilizing the information from the user profile.

Claims

1. A method of speech recognition comprising:

creating a user profile for large vocabulary continuous speech recognition without using an enrollment procedure; and

performing large vocabulary continuous speech recognition of unknown speech inputs from the user utilizing the information from the user profile.

2. A method according to claim 1, wherein the performing large vocabulary continuous speech recognition includes performing unsupervised adaptation of the user profile.

3. A method according to claim 2, wherein the adaptation is a feature space adaptation.

4. A method according to claim 2, wherein the adaptation is a model space adaptation.

5. A method according to claim 2, wherein the adaptation includes accumulating adaptation statistics after each utterance recognition.

6. A method according to claim 5, wherein the adaptation statistics are computed based on the speech input of the utterance and the corresponding recognition result.

7. A method according to claim 2, wherein an adaptation transform is updated after every number M utterance recognitions.

8. A method according to claim 2, wherein some number T seconds worth of recognition statistics are required to update the adaptation transform.

9. A method according to claim 2, wherein the adaptation is based on Constrained Maximum Likelihood Linear Regression (CMLLR) adaptation.

10. A method according to claim 9, wherein the CMLLR adaptation includes updating a CMLLR transform using adaptation statistics accumulated with a forgetting factor.

11. A method according to claim 10, wherein the forgetting factor is based on multiplying an accumulated statistic by a configurable factor after the statistic has been used to update the CMLLR transform some number N times.

12. A method according to claim 9, wherein the CMLLR adaptation includes updating a CMLLR transform using adaptation statistics accumulated using some fraction F of highest probability Gaussian components of aligned hidden Markov model states.

13. A method according to claim 9, wherein the CMLLR adaptation includes using a CMLLR transform which is initialized from a pre-existing transform when a new transform is computed.

14. A method according to claim 9, wherein the CMLLR adaptation includes using a CMLLR transform which is initialized from an inverse of an MLLR transform.

15. A method according to claim 2, wherein performing unsupervised adaptation is coordinated with processor load so as to minimize recognition latency effects.

16. A method according to claim 1, wherein the user profile includes a stable transform based on supervised or unsupervised adaptation for modeling relatively static acoustic characteristics.

17. A method according to claim 16, wherein the transform is based on Constrained Maximum Likelihood Linear Regression (CMLLR).

18. A method according to claim 1, wherein the user profile includes a dynamic transform based on unsupervised adaptation for modeling relatively dynamic acoustic characteristics.

19. A method according to claim 18, wherein the transform is based on Constrained Maximum Likelihood Linear Regression (CMLLR).

20. A method according to claim 1, wherein the user profile includes speaker dependent acoustic models based on model space adaptation.

21. A method according to claim 20, wherein the transform is based on Constrained Maximum Likelihood Linear Regression (CMLLR).

22. A method according to claim 1, further comprising:

updating the user profile using unknown speech inputs and the corresponding recognized texts.

23. A method according to claim 1, wherein the speech recognition uses scaled integer arithmetic.

24. A system for speech recognition comprising:

means for creating a user profile for large vocabulary continuous speech recognition without first requiring an enrollment procedure, the user profile including speech recognition information associated with a specific user; and

means for performing large vocabulary continuous speech recognition of unknown speech inputs from the user utilizing the information from the user profile.

25. A system according to claim 24, wherein the means for performing large vocabulary continuous speech recognition includes means for performing unsupervised adaptation of the user profile.

26. A system according to claim 25, wherein the adaptation is a feature space adaptation.

27. A system according to claim 25, wherein the adaptation is a model space adaptation.

28. A system according to claim 25, wherein the means for performing unsupervised adaptation accumulates adaptation statistics after each utterance recognition.

29. A system according to claim 28, wherein the adaptation statistics are computed based on the speech input of the utterance and the corresponding recognition result.

30. A system according to claim 25, wherein the means for performing unsupervised adaptation updates an adaptation transform after some number M utterance recognitions.

31. A system according to claim 25, wherein the means for performing unsupervised adaptation requires some number T seconds worth of adaptation statistics to update the adaptation transform.

32. A system according to claim 25, wherein the means for performing unsupervised adaptation is based on a Constrained Maximum Likelihood Linear Regression (CMLLR) adaptation.

33. A system according to claim 32, wherein the means for performing unsupervised adaptation includes means for updating a CMLLR transform using adaptation statistics accumulated with a forgetting factor.

34. A system according to claim 33, wherein the forgetting factor is based on multiplying an accumulated statistic by a configurable factor after the statistic has been used to update the CMLLR transform some number N times.

35. A system according to claim 32, wherein the means for performing unsupervised adaptation includes means for updating a CMLLR transform using adaptation statistics accumulated using some fraction F of highest probability Gaussian components of aligned hidden Markov model states.

36. A system according to claim 32, wherein the means for performing unsupervised adaptation uses a CMLLR transformation which is initialized from a pre-existing transform when a new transform is computed.

37. A system according to claim 32, wherein the means for performing unsupervised adaptation uses a CMLLR transformation which is initialized from an inverse of an MLLR transform.

38. A system according to claim 25, wherein the means for performing unsupervised adaptation coordinates with processor load so as to minimize recognition latency effects.

39. A system according to claim 24, wherein the user profile includes a stable transform based on supervised or unsupervised adaptation for modeling relatively static acoustic characteristics.

40. A system according to claim 39, wherein the transform is based on Constrained Maximum Likelihood Linear Regression (CMLLR).

41. A system according to claim 24, wherein the user profile includes a dynamic transform based on unsupervised adaptation for modeling relatively dynamic acoustic characteristics.

42. A system according to claim 41, wherein the transform is based on Constrained Maximum Likelihood Linear Regression (CMLLR).

43. A system according to claim 24, wherein the user profile includes speaker dependent acoustic models based on model space adaptation.

44. A system according to claim 43, wherein the transform is based on Constrained Maximum Likelihood Linear Regression (CMLLR).

45. A method according to claim 24, further comprising:

means for updating the user profile using unknown speech inputs and the corresponding recognized texts.

46. A system according to claim 24, wherein the means for performing large vocabulary continuous speech recognition uses scaled integer arithmetic.