System and method for tying variance vectors for speech recognition

• US 20060136210A1
• Filed: 12/16/2004
• Published: 06/22/2006
• Est. Priority Date: 12/16/2004
• Status: Abandoned Application

• Current Assignee: Sony Corporation (Sony Group Corp.), Sony Electronics Inc. (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.), Sony Electronics Inc. (Sony Group Corp.)
• Inventors: Menendez-Pidal, Xavier, Patrikar, Ajay Madhukar
• Application Number:
  US11/014,462
• Publication Number:
  US 20060136210A1
• Time in Patent Office: Days
• US Class Current: 704/256.800
• CPC Class Codes: G10L 15/144 Training of HMMs

Abstract

A system and method for implementing a speech recognition engine includes acoustic models that the speech recognition engine utilizes to perform speech recognition procedures. An acoustic model optimizer performs a vector quantization procedure upon original variance vectors initially associated with the acoustic models. In certain embodiments, the vector quantization procedure may be performed as a block vector quantization procedure or as a subgroup vector quantization procedure. The vector quantization procedure produces a reduced number of tied variance vectors for optimally implementing the acoustic models.

Claims

1. A system for implementing a speech recognition engine, comprising:

acoustic models that said speech recognition engine utilizes to perform speech recognition procedures; and

an acoustic model optimizer that performs a vector quantization procedure upon original variance vectors initially associated with said acoustic models, said vector quantization procedure producing a number of compressed variance vectors less than the number of said original variance vectors, said compressed variance vectors then being used in said acoustic models in place of said original variance vectors.

2. The system of claim 1 wherein said vector quantization procedure is performed as a block vector quantization procedure that operates upon all of said original variance vectors to produce a set of said compressed variance vectors.

3. The system of claim 1 wherein said vector quantization procedure is performed as a plurality of subgroup vector quantization procedures that each operates upon a different subgroup of said original variance vectors to produce corresponding subgroups of said compressed variance vectors.

4. The system of claim 1 wherein said acoustic models represent phones from a phone set utilized by said speech recognition engine.

5. The system of claim 1 wherein said original variance vectors and said compressed variance vectors are each implemented to include a different set of individual variance parameters.

6. The system of claim 1 wherein each of said acoustic models is implemented to include a sequence of model states that represent a corresponding phone supported by said speech recognition engine.

7. The system of claim 6 wherein each of said model states includes one or more Gaussians with corresponding mean vectors.

8. The system of claim 7 wherein each of said compressed variance vectors from said vector quantization procedure corresponds to a plurality of said means vectors.

9. The system of claim 1 wherein said compressed variance vectors require less memory resources than said original variance vectors.

10. The system of claim 1 wherein a set of original acoustic models are trained using a training database before performing a block vector quantization procedure.

11. The system of claim 10 wherein a vector compression target value is defined to specify a final target number of said compressed variance vectors.

12. The system of claim 1 wherein said acoustic model optimizer accesses, as a single block unit, all of said original variance vectors from said original acoustic models.

13. The system of claim 12 wherein said acoustic model optimizer collectively performs said block vector quantization procedure upon said single block unit of said original variance vectors to produce a composite set of said compressed variance vectors for implementing said optimized acoustic models.

14. The system of claim 1 wherein a subgroup category is initially defined to specify a granularity level for performing subgroup vector quantization procedures.

15. The system of claim 14 wherein said subgroup category is defined at a phone level.

16. The system of claim 14 wherein said subgroup category is defined at a state-cluster level.

17. The system of claim 14 wherein said subgroup category is defined at a state level.

18. The system of claim 14 wherein said acoustic model optimizer separately accesses subgroups of said original variance vectors according to said subgroup category.

19. The system of claim 14 wherein a vector compression factor is defined to specify a compression rate for performing said subgroup vector quantization procedure upon subgroups of said original variance vectors.

20. The system of claim 14 wherein said acoustic model optimizer performs separate subgroup vector quantization procedures upon selected subgroups of said original variance vectors to produce corresponding compressed subgroups of said compressed variance vectors.

21. A method for implementing a speech recognition engine, comprising:

defining acoustic models for performing speech recognition procedures; and

utilizing an acoustic model optimizer to perform a vector quantization procedure upon original variance vectors initially associated with said acoustic models, said vector quantization procedure producing a number of compressed variance vectors less than the number of said original variance vectors, said compressed variance vectors then being used in said acoustic models in place of said original variance vectors.

22. The method of claim 21 wherein said vector quantization procedure is performed as a block vector quantization procedure that operates upon all of said original variance vectors to produce a set of said compressed variance vectors.

23. The method of claim 21 wherein said vector quantization procedure is performed as a plurality of subgroup vector quantization procedures that each operates upon a different subgroup of said original variance vectors to produce corresponding subgroups of said compressed variance vectors.

24. The method of claim 21 wherein said acoustic models represent phones from a phone set utilized by said speech recognition engine.

25. The method of claim 21 wherein said original variance vectors and said compressed variance vectors are each implemented to include a different set of individual variance parameters.

26. The method of claim 21 wherein each of said acoustic models is implemented to include a sequence of model states that represent a corresponding phone supported by said speech recognition engine.

27. The method of claim 26 wherein each of said model states includes one or more Gaussians with corresponding mean vectors.

28. The method of claim 27 wherein each of said compressed variance vectors from said vector quantization procedure corresponds to a plurality of said means vectors.

29. The method of claim 21 wherein said compressed variance vectors require less memory resources than said original variance vectors.

30. The method of claim 21 wherein a set of original acoustic models are trained using a training database before performing a block vector quantization procedure.

31. The method of claim 30 wherein a vector compression target value is defined to specify a final target number of said compressed variance vectors.

32. The method of claim 21 wherein said acoustic model optimizer accesses, as a single block unit, all of said original variance vectors from said original acoustic models.

33. The method of claim 32 wherein said acoustic model optimizer collectively performs said block vector quantization procedure upon said single block unit of said original variance vectors to produce a composite set of said compressed variance vectors for implementing said optimized acoustic models.

34. The method of claim 21 wherein a subgroup category is initially defined to specify a granularity level for performing subgroup vector quantization procedures.

35. The method of claim 34 wherein said subgroup category is defined at a phone level.

36. The method of claim 34 wherein said subgroup category is defined at a state-cluster level.

37. The method of claim 34 wherein said subgroup category is defined at a state level.

38. The method of claim 34 wherein said acoustic model optimizer separately accesses subgroups of said original variance vectors according to said subgroup category.

39. The method of claim 34 wherein a vector compression factor is defined to specify a compression rate for performing said subgroup vector quantization procedure upon subgroups of said original variance vectors.

40. The method of claim 34 wherein said acoustic model optimizer performs separate subgroup vector quantization procedures upon selected subgroups of said original variance vectors to produce corresponding compressed subgroups of said compressed variance vectors.

41. A system for implementing a speech recognition engine, comprising:

means for defining acoustic models to perform speech recognition procedures; and

means for performing a vector quantization procedure upon original variance vectors initially associated with said acoustic models, said vector quantization procedure producing a number of compressed variance vectors less than the number of said original variance vectors, said compressed variance vectors then being used in said acoustic models in place of said original variance vectors.