Speech processing system

• US 7,010,483 B2
• Filed: 05/30/2001
• Issued: 03/07/2006
• Est. Priority Date: 06/02/2000
• Status: Expired due to Fees

• Current Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Original Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Inventors: Rajan, Jebu Jacob
• Primary Examiner(s): Lerner, Martin
• Application Number:
  US09/866,854
• Publication Number:
  US 20020026309A1
• Time in Patent Office: 1,742 Days
• Field of Search: 704/226, 704/227, 704/228, 704/233, 704/234, 704/240, 714/746
• US Class Current: 704/228
• CPC Class Codes: G10L 25/69 for evaluating synthetic or decoded voice signals

Abstract

A speech processing system is provided which is operable to receive sets of signal values representative of a speech signal generated by a speech source. The system is operable to determine a measure of the quality of the speech signal by performing a statistical analysis of the received sets of signal values. The system stores data defining a predetermined function derived from a signal model which models the speech source and which defines a probability density function which gives, for a given set of model parameters, the probability that the signal model has those model parameters given that the signal model is assumed to have generated the received set of signal values. The system applies a current set of received signal values to the stored probability density function and then draws samples from it using a Gibbs sampler. The system then analyses the samples to determine a measure of the variance of some of the samples and then outputs a signal indicative of the quality of the received speech signal values in dependence upon the determined variance.

Claims

1. An apparatus for determining a quality measure indicative of the quality of a speech signal, the apparatus comprising:

a receiver operable to receive a set of speech signal values representative of a speech signal generated by a speech source as distorted by a transmission channel between the speech source and the receiver;

a memory operable to store a predetermined function which includes a first part having first parameters which models said source and a second part having second parameters which models said channel and which gives, for a given set of speech signal values, a probability density for parameters of a predetermined speech model which is assumed to have generated the set of speech signal values, the probability density defining, for a given set of model parameter values, the probability that the predetermined speech model has those parameter values, given that the model is assumed to have generated the set of speech signal values;

an applicator operable to apply the set of received speech signal values to said stored function to give the probability density for said model parameters for the set of received speech signal values;

a processor operable to process said function with said set of received speech signal values applied, to derive samples of at least said first parameters from said probability density;

an analyser operable to analyse at least some of said derived samples of said at least first parameters to determine a quality measure indicative of the quality of the received speech signal values; and

an output operable to output values of said first parameters that are representative of said speech signal generated by said speech source before it was distorted by said transmission channel.

2. An apparatus according to claim 1, wherein said analyser is operable to determine a measure of the variance of said at least some of said derived samples of said at least first parameters to determine said quality measure.

3. An apparatus according to claim 2, wherein said probability density function is in terms of said variance measure and wherein said processor is operable to draw samples of said variance measure from said probability density function.

4. An apparatus according to claim 3, wherein said processor comprises a Gibbs sampler.

5. An apparatus according to claim 3, wherein said analyser is operable to determine a histogram of said drawn samples and wherein said quality measure is determined using said histogram.

6. An apparatus according to claim 5, wherein said analyser is operable to determine said quality measure using a weighted sum of said drawn samples, and wherein the weighting for each sample is determined from said histogram.

7. An apparatus according to claim 1, wherein said processor is operable to draw samples iteratively from said probability density function.

8. An apparatus according to claim 1, wherein said receiver is operable to receive a sequence of sets of speech signal values representative of an input speech signal and wherein said applicator, processor and analyser are operable to perform their respective functions with respect to each set of received speech signal values to determine a quality measure for each set of received signal values.

9. An apparatus according to claim 8, wherein said processor is operable to use the values of parameters obtained during the processing of a preceding set of signal values as initial estimates for the values of the corresponding parameters for a current set of signal values being processed.

10. An apparatus according to claim 8, wherein said sets of signal values in said sequence are non-overlapping.

11. An apparatus according to claim 1, wherein said speech model comprises an auto-regressive process model and wherein said parameters include auto-regressive model coefficients.

12. An apparatus according to claim 1, wherein said speech signal model includes a noise model having a noise parameter and wherein said quality measure is determined using said noise parameter.

13. An apparatus according to claim 1, wherein said processor is operable to determine a histogram of said derived samples and wherein said values of said first parameters are determined from said histogram.

14. An apparatus according to claim 13, wherein said processor is operable to determine said values of said first parameters using a weighted sum of said derived samples, and wherein the weighting for each sample is determined from said histogram.

15. An apparatus according to claim 1, wherein said processor is operable to derive samples of said second parameters and wherein said analyser is operable to determine said quality measure using the derived samples of said second parameters.

16. An apparatus according to claim 1, wherein said function is in terms of a set of raw speech signal values representative of speech generated by said source before being distorted by said transmission channel, wherein the apparatus further comprises a second processor operable to process the received set of signal values with initial estimates of said first and second parameters, to generate an estimate of the raw speech signal values corresponding to the received set of signal values and wherein said applicator is operable to apply said estimated set of raw speech signal values to said function in addition to said set of received signal values.

17. An apparatus according to claim 16, wherein said second processor comprises a simulation smoother.

18. An apparatus according to claim 16, wherein said second processor comprises a Kalman filter.

19. An apparatus according to claim 1, wherein said second part is a moving average model and said second parameters comprise moving average model coefficients.

20. An apparatus according to claim 1, further comprising a comparator responsive to said quality measure and operable to compare signals representative of the received speech signal with prestored models, to generate a comparison result.

21. An apparatus according to claim 20, wherein said signals representative of the speech signal are derived from said stored function.

22. An apparatus according to claim 1, further comprising an encoder operable to encode signals representative of the speech signal in dependence upon the output quality measure.

23. An apparatus for generating annotation data for use in annotating a data file, the apparatus comprising:

a receiver operable to receive a speech annotation;

an apparatus according to claim 1 for generating a quality measure indicative of the quality of the received speech annotation; and

a generator operable to generate annotation data using data representative of the received speech annotation and said quality measure.

24. An apparatus according to claim 23, further comprising a speech recogniser operable to process the speech annotation to identify words and/or phonemes within the speech annotation, wherein said annotation data comprises data identifying said words and/or phonemes.

25. An apparatus according to claim 24, wherein said data representative of the received speech annotation is derived using said apparatus according to claim 1.

26. An apparatus according to claim 25, wherein said annotation data defines a phoneme and word lattice.

27. An apparatus for searching a database comprising a plurality of information entries to identify information to be retrieved therefrom, each of said plurality of information entries having an associated annotation and a quality measure indicative of the quality of the annotation;

a receiver operable to receive an input speech query;

an apparatus according to claim 1 for processing said input speech query to generate a quality measure therefor; and

a comparator operable to compare data representative of the input speech query with said annotations in dependence upon the quality measure of said input speech query and the corresponding quality measures of said annotations.

28. An apparatus for searching a database comprising a plurality of annotations which include annotation data and a quality measure indicative of the quality of an annotation used to generate the annotation data, the apparatus comprising:

means for receiving an input audio query;

means for determining a quality measure for the input audio query; and

means for comparing data representative of said input query with the annotation data of one or more of said annotations in dependence upon the quality measure for said input query and the corresponding quality measure for the annotation.

29. An apparatus according to claim 28, wherein said data representative of said input query and said annotation data comprise word and/or phoneme data.

30. An apparatus according to claim 28, wherein said comparing means is operable to compare said query data with said annotation data using a first comparison technique if both said quality measures exceed a predetermined threshold and is operable to compare said query data with said annotation data using a second comparison technique if either or both of said quality measures are below said predetermined threshold.

31. A method of determining a quality measure indicative of the quality of a speech signal, the method comprising the steps of:

receiving, at a receiver, a set of speech signal values representative of a speech signal generated by a speech source as distorted by a transmission channel between the speech source and the receiver;

storing a predetermined function which includes a first part having first parameters which models said source and a second part having second parameters which models said channel and which gives, for a given set of speech signal values, a probability density for parameters of a predetermined speech model which is assumed to have generated the set of speech signal values, the probability density defining, for a given set of model parameter values, the probability that the predetermined speech model has those parameter values, given that the model is assumed to have generated the set of speech signal values;

applying the set of received speech signal values to said stored function to give the probability density for said model parameters for the set of received speech signal values;

processing said function with said set of received speech signal values applied, to derive samples of at least said first parameters from said probability density;

analysing at least some of said derived samples of said at least first parameters to determine a quality measure indicative of the quality of the received speech signal values; and

outputting values of said first parameters that are representative of said speech signal generated by said speech source before it was distorted by said transmission channel.

32. A method according to claim 31, wherein said analysing step determines a measure of the variance of said at least some of said derived samples of said at least first parameters in determining said quality measure.

33. A method according to claim 32, wherein said probability density function is in terms of said variance measure and wherein said processing step draws samples of said variance measure from said probability density function.

34. A method according to claim 33, wherein said processing step uses a Gibbs sampler.

35. A method according to claim 33, wherein said analysing step determines a histogram of said drawn samples and wherein said quality measure is determined using said histogram.

36. A method according to claim 35, wherein said analysing step determines said quality measure using a weighted sum of said drawn samples, and wherein the weighting for each sample is determined from said histogram.

37. A method according to claim 31, wherein said processing step draws samples iteratively from said probability density function.

38. A method according to claim 31, wherein said receiving step receives a sequence of sets of speech signal values representative of an input speech signal and wherein said applying step, processing step, and analysing step are performed with respect to each set of received speech signal values to determine a quality measure for each set of received signal values.

39. A method according to claim 38, wherein said processing step uses the values of parameters obtained during the processing of a preceding set of signal values as initial estimates for the values of the corresponding parameters for a current set of signal values being processed.

40. A method according to claim 38, wherein said sets of signal values in said sequence are non-overlapping.

41. A method according to claim 31, wherein said speech model comprises an auto-regressive process model and wherein said parameters include auto-regressive model coefficients.

42. A method according to claim 31, wherein said speech signal model includes a noise model having a noise parameter and wherein said quality measure is determined using said noise parameter.

43. A method according to claim 31, wherein said processing step determines a histogram of said derived samples and wherein said values of said first parameters are determined from said histogram.

44. A method according to claim 43, wherein said processing step determines said values of said first parameters using a weighted sum of said derived samples, and wherein the weighting for each sample is determined from said histogram.

45. A method according to claim 31, wherein said processing step derives samples of said second parameters and wherein said analysing step determines said quality measure using the derived samples of said second parameters.

46. A method according to claim 31, wherein said function is in terms of a set of raw speech signal values representative of speech generated by said source before being distorted by said transmission channel, wherein the method further comprises a second processing step of processing the received set of signal values with initial estimates of said first and second parameters, to generate an estimate of the raw speech signal values corresponding to the received set of signal values and wherein said applying step applies said estimated set of raw speech signal values to said function in addition to said set of received signal values.

47. A method according to claim 46, wherein said second processing step uses a simulation smoother.

48. A method according to claim 46, wherein said second processing step uses a Kalman filter.

49. A method according to claim 31, wherein said second part is a moving average model and said second parameters comprise moving average model coefficients.

50. A method according to claim 31, further comprising a step of comparing signals representative of the received speech signal with prestored models to generate a comparison result and wherein said comparing step is responsive to said quality measure.

51. A method according to claim 50, wherein said signals representative of the speech signal are derived from said stored function.

52. A method according to claim 31, further comprising a step of encoding signals representative of the speech signal in dependence upon the output quality measure.

53. A method of generating annotation data for use in annotating a data file, the method comprising the steps of:

receiving a speech annotation;

performing the method according to claim 31 to generate a quality measure indicative of the quality of the received speech annotation; and

generating annotation data using data representative of the received speech annotation and said quality measure.

54. A method according to claim 53, further comprising a step of using a speech recognition unit to process the speech annotation to identify words and/or phonemes within the speech annotation, wherein said annotation data comprises said words and/or phonemes.

55. A method according to claim 54, wherein said data representative of the received speech annotation is derived using said method according to claim 31.

56. A method according to claim 55, wherein said annotation data defines a phoneme and word lattice.

57. A method of searching a database comprising a plurality of information entries to identify information to be retrieved therefrom, each of said plurality of information entries having an associated annotation and a quality measure indicative of the quality of the annotation, the method comprising the steps of:

receiving an input speech query;

using the method according to claim 31 to process said input speech query to generate a quality measure therefor; and

comparing data representative of the input speech query with said annotations in dependence upon the quality measure of said input speech query and the corresponding quality measures of said annotations.

58. A computer readable medium storing computer executable process steps to cause a programmable computer apparatus to perform the method according to claim 31.

59. Processor implementable process steps for causing a programmable computing device to perform the method according to claim 31.

60. A method of searching a database comprising a plurality of annotations which include annotation data and a quality measure indicative of the quality of an annotation used to generate the annotation data, the method comprising the steps of:

receiving an input audio query;

determining a quality measure for the input audio query; and

comparing data representative of said input query with the annotation data of one or more of said annotations in dependence upon the quality measure for said input query and the corresponding quality measure for the annotation.

61. A method according to claim 60, wherein said data representative of said input query and said annotation data comprise word and/or phoneme data.

62. A method according to claim 60, wherein said comparing step compares said query data with said annotation data using a first comparison technique if both said quality measures exceed a predetermined threshold and compares said query data with said annotation data using a second comparison technique if either or both of said quality measures are below said predetermined threshold.

63. An apparatus for determining a quality measure indicative of the quality of a speech signal, the apparatus comprising:

means for receiving a set of speech signal values representative of a speech signal generated by a speech source as distorted by a transmission channel between the speech source and the receiving means;

a memory for storing a predetermined function which includes a first part having first parameters which models said source and a second part having second parameters which models said channel and which gives, for a given set of speech signal values, a probability density for parameters of a predetermined speech model which is assumed to have generated the set of speech signal values, the probability density defining, for a given set of model parameter values, the probability that the predetermined speech model has those parameter values, given that the model is assumed to have generated the set of speech signal values;

means for applying the set of received speech signal values to said stored function to give the probability density for said model parameters for the set of received speech signal values;

means for processing said function with said set of received speech signal values applied, to derive samples of at least said first parameters from said probability density;

means for analysing at least some of said derived samples of said at least first parameters to determine a quality measure indicative of the quality of the received speech signal values; and

means for outputting values of said first parameters that are representative of said speech signal generated by said speech source before it was distorted by said transmission channel.

64. An apparatus for generating annotation data for use in annotating a data file, the apparatus comprising:

means for receiving a speech annotation;

an apparatus according to claim 63 for generating a quality measure indicative of the quality of the received speech annotation; and

means for generating annotation data using data representative of the received speech annotation and said quality measure.

65. An apparatus for searching a database comprising a plurality of information entries to identify information to be retrieved therefrom, each of said plurality of information entries having an associated annotation and a quality measure indicative of the quality of the annotation;

means for receiving an input speech query;

an apparatus according to claim 63 for processing said input speech query to generate a quality measure therefor; and

means for comparing data representative of the input speech query with said annotations in dependence upon the quality measure of said input speech query and the corresponding quality measures of said annotations.