Data process unit and data process unit control program

• US 20050075875A1
• Filed: 07/22/2004
• Published: 04/07/2005
• Est. Priority Date: 10/03/2003
• Status: Active Grant

• Current Assignee: Asahi Kasei Kabushiki Kaisha (Asahi Kasei Corporation)
• Original Assignee: Asahi Kasei Kabushiki Kaisha (Asahi Kasei Corporation)
• Inventors: Shozakai, Makoto, Nagino, Goshu
• Granted Patent: US 7,548,651 B2
• Time in Patent Office: Days
• US Class Current: 704/231
• CPC Class Codes: G06F 18/2163 Partitioning the feature space
• CPC Class Codes: G06V 10/765 using rules for classification or partitioning the feature space
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/07 to the speaker
• CPC Class Codes: G10L 15/144 Training of HMMs
• CPC Class Codes: G10L 21/06 Transformation of speech into a non-audible representation, e.g. speech visualisation or speech processing for tactile aids G10L15/26 takes precedence

Abstract

To provide a data process unit and data process unit control program which are suitable for generating acoustic models for unspecified speakers taking distribution of diversifying feature parameters into consideration under such specific conditions as the type of speaker, speech lexicons, speech styles, and speech environment and which are suitable for providing acoustic models intended for unspecified speakers and adapted to speech of a specific person.

A data process unit 1 comprises a data classification section 1a, data storing section 1b, pattern model generating section 1c, data control section 1d, mathematical distance calculating section 1e, pattern model converting section 1f, pattern model display section 1g, region dividing section 1h, division changing section 1i, region selecting section 1j, and specific pattern model generating section 1k.

Claims

1. A data process unit comprising:

data classification means for classifying a plurality of predetermined data on a plurality of objects into a plurality of groups based on a plurality of specific conditions;

pattern model generating means for generating a plurality of pattern models which have 4-dimensional or higher dimensional elements for each group of predetermined data based on the predetermined data classified by the data classification means;

mathematical distance calculating means for calculating mathematical distance among the pattern models generated by the pattern model generating means for the respective groups;

pattern model converting means for converting the plurality of pattern models into the same number of low dimensional vectors corresponding to the pattern models in the lower dimension while maintaining distance relationship among the pattern models, based on the mathematical distance calculated by the mathematical distance calculating means; and

low dimensional vector corresponding to pattern model display means for displaying the plurality of low dimensional vectors corresponding to pattern models as coordinate points in a low dimensional space of the same dimension as the low dimensional vectors corresponding to pattern models while maintaining the distance relationship, based on values of low dimensional elements.

2. The data process unit according to claim 1, wherein the plurality of predetermined data on a plurality of objects are one of human voice data, ambient noise data, animal sound data, image data, infrared data, and acceleration data.

3. The data process unit according to claim 1, wherein:

the plurality of predetermined data on a plurality of objects are data on a plurality of speech sounds produced by a plurality of speakers; and

the plurality of specific conditions include at least, the type of speaker, speech lexicons, speech styles, and speech environment.

4. The data process unit according to claim 1 or claim 2, wherein the data classification means forms a group of predetermined data under each combination of specific conditions selected freely from the plurality of specific conditions.

5. The data process unit according to any of claims 1 to 3, wherein the pattern models are generated using HMMs (Hidden Markov Models).

6. The data process unit according to claim 5, wherein the mathematical distance calculating means calculates the mathematical distance using one of Euclidean distance determined based on a mean vector of normal distributions of the pattern models generated using the HMMs, Euclidean distance determined based on a mean vector of normal distributions of the pattern models normalized by the standard deviation of the normal distributions of the pattern models, and Bhattacharrya distance determined based on normal distributions of the pattern models.

7. The data process unit according to any of claims 1 to 6, wherein the pattern model converting means converts the pattern models into the low dimensional vectors corresponding to the pattern models using Sammon'"'"'s method.

8. The data process unit according to any of claims 1 to 7, comprising region dividing means for automatically dividing the coordinate points of the plurality of low dimensional vectors corresponding to pattern models displayed in the low dimensional space by the low dimensional vector corresponding to pattern model display means into a plurality of regions in the low dimensional space.

9. The data process unit according to claim 8, wherein the region dividing means divides the plurality of low dimensional vectors corresponding to pattern models by an outer circle and n inner circles (n is an integer equal to or larger than 1) and further divides ring-shaped regions formed by the concentric outer and inner circles therebetween into a plurality of regions by lines extending radially, where the outer circle is centered at the center of gravity of the coordinate points of all the low dimensional vectors corresponding to pattern models and has a radius equal to the distance between the center of gravity and the coordinate point of the low dimensional vector corresponding to pattern model farthest from the center of gravity while the inner circle is centered at the center of gravity and has a radius smaller than that of the outer circle.

10. The data process unit according to claim 8 or 9, comprising division changing means for changing the results of divisions produced automatically by the region dividing means.

11. The data process unit according to any of claims 8 to 10, comprising:

region selecting means capable of selecting a plurality of low dimensional vectors corresponding to pattern models for each region produced by region dividing means; and

regional pattern model generating means for generating pattern models based on the predetermined data related to the pattern models corresponding to the low dimensional vectors corresponding to the pattern models located in the region selected by the region selecting means.

12. A data process unit control program which is a computer-executable program for controlling the data process unit according to claim 1, comprising:

a data classification step of classifying a plurality of predetermined data on a plurality of objects into a plurality of groups based on a plurality of specific conditions;

a pattern model generating step of generating a plurality of pattern models which have 4-dimensional or higher dimensional elements for each group of predetermined data based on the predetermined data classified in the data classification step;

a mathematical distance calculating step of calculating mathematical distance among the pattern models generated in the pattern model generating step of the respective groups;

a pattern model converting step of converting the plurality of pattern models into the same number of low dimensional vectors corresponding to the pattern models while maintaining distance relationship among the pattern models, based on the mathematical distance calculated in the mathematical distance calculating step; and

a low dimensional vector corresponding to pattern model display step of displaying the plurality of low dimensional vectors corresponding to pattern models as coordinate points in a low dimensional space of the same dimension as the low dimensional vectors corresponding to pattern models while maintaining the distance relationship, based on values of low dimensional elements.

13. The data process unit control program according to claim 12 comprising:

a region dividing step of automatically dividing the plurality of low dimensional vectors corresponding to pattern models displayed in the low dimensional space in the low dimensional vector corresponding to pattern model display step into a plurality of regions in the low dimensional space;

a division changing step of changing the results of divisions produced automatically in the region dividing step;

a region selecting step capable of selecting a plurality of low dimensional vectors corresponding to pattern models for each region produced in region dividing step; and

a regional pattern model generating step of generating pattern models corresponding to the selected region based on the predetermined data related to the pattern models corresponding to the low dimensional vectors corresponding to pattern models located in the region selected in the region selecting step.

14. A pattern model search unit comprising:

region dividing means for automatically dividing the coordinate points displayed in the low dimensional space by the data process unit according to any of claims 1 to 7 into a plurality of regions in the low dimensional space;

regional pattern model generating means for generating pattern models for each region based on predetermined data corresponding to coordinate points of the low dimensional vectors corresponding to pattern models contained in the segment regions;

predetermined-data acquiring means for acquiring predetermined data on a new object; and

regional pattern model searching means for calculating likelihood of regional pattern models for respective segment regions in relation to the acquired predetermined data and searching for a regional pattern model with recognition performance suitable for recognizing the predetermined data on the new object based on the calculated likelihood.

15. A pattern model search unit control program which is a computer-executable program for controlling the pattern model search unit according to claim 14, comprising:

a region dividing step of automatically dividing the coordinate points displayed in the low dimensional space by the speech data process unit according to any of claims 1 to 7 into a plurality of regions in the low dimensional space;

a regional pattern model generating step of generating pattern models for each region based on predetermined data corresponding to coordinate points of the low dimensional vectors corresponding to pattern models contained in the segment regions;

a predetermined-data acquiring step of acquiring predetermined data on a new object; and

a regional pattern model searching step of calculating likelihood of regional pattern models corresponding to respective segment regions in relation to the acquired predetermined data and searching for a regional pattern model with recognition performance suitable for recognizing the predetermined data on the new object based on the calculated likelihood.

16. A data process unit comprising:

data classification means for classifying a plurality of predetermined data on a plurality of objects into a plurality of groups based on a plurality of specific conditions;

pattern model generating means for generating a plurality of pattern models which have 4-dimensional or higher dimensional elements for each group of predetermined data based on the predetermined data classified by the data classification means;

mathematical distance calculating means for calculating mathematical distance among the pattern models generated by the pattern model generating means for the respective groups;

pattern model converting means for converting the plurality of pattern models into the same number of low dimensional vectors corresponding to the pattern models in the lower dimension while maintaining distance relationship among the pattern models, based on the mathematical distance calculated by the mathematical distance calculating means; and

low dimensional vector corresponding to pattern model display means for displaying the plurality of low dimensional vectors corresponding to pattern models as coordinate points in a low dimensional space of the same dimension as the low dimensional vectors corresponding to pattern models while maintaining the distance relationship, based on values of low dimensional elements, wherein

the mathematical distance calculating means uses the occurrence frequency of each pattern unit in the plurality of predetermined data on the plurality of objects when calculating the mathematical distance.

17. The data process unit according to claim 16, wherein the occurrence frequency of the pattern units is the occurrence frequency of pattern units whose recognition performance is lower than a predetermined threshold from among a plurality of predetermined data related to the plurality of objects in pattern recognition of the plurality of predetermined data using the pattern models.

35. A data process unit control program which is a computer-executable program for controlling the data process unit according to claim 16, comprising:

a data classification step of classifying a plurality of predetermined data on a plurality of objects into a plurality of groups based on a plurality of specific conditions;

a pattern model generating step of generating a plurality of pattern models which have 4-dimensional or higher dimensional elements for each group of predetermined data based on the predetermined data classified in the data classification step;

a mathematical distance calculating step of calculating mathematical distance among the pattern models generated in the pattern model generating step of the respective groups;

a pattern model converting step of converting the plurality of pattern models into the same number of low dimensional vectors corresponding to the pattern models in the lower dimension while maintaining distance relationship among the pattern models, based on the mathematical distance calculated in the mathematical distance calculating step; and

a low dimensional vector corresponding to pattern model display step of displaying the plurality of low dimensional vectors corresponding to pattern models as coordinate points in a low dimensional space of the same dimension as the low dimensional vectors corresponding to pattern models while maintaining the distance relationship, based on values of low dimensional elements, wherein

the mathematical distance calculating step uses the occurrence frequency of each pattern unit in the plurality of predetermined data on the plurality of objects when calculating the mathematical distance.

18. A data process unit comprising:

data classification means for classifying a plurality of predetermined data on a plurality of objects into a plurality of groups based on a plurality of specific conditions;

pattern model generating means for generating a plurality of pattern models which have 4-dimensional or higher dimensional elements for each group of predetermined data based on the predetermined data classified by the data classification means;

mathematical distance calculating means for calculating mathematical distance among the pattern models generated by the pattern model generating means for the respective groups;

pattern model converting means for converting the plurality of pattern models into the same number of low dimensional vectors corresponding to the pattern models while maintaining distance relationship among the pattern models, based on the mathematical distance calculated by the mathematical distance calculating means;

low dimensional vector corresponding to pattern model display means for displaying the plurality of low dimensional vectors corresponding to pattern models as coordinate points in a low dimensional space of the same dimension as the low dimensional vectors corresponding to pattern models while maintaining the distance relationship, based on values of low dimensional elements;

region dividing means for automatically dividing the coordinate points of the plurality of low dimensional vectors corresponding to pattern models displayed in the low dimensional space by the low dimensional vector corresponding to pattern model display means into a plurality of regions in the low dimensional space;

regional pattern model generating means for generating regional pattern models for each region based on predetermined data corresponding to coordinate points of the low dimensional vectors corresponding to pattern models contained in the segment regions;

predetermined-data acquiring means for acquiring predetermined data on a new object; and

regional pattern model searching means for calculating likelihood of regional pattern models for respective segment regions in relation to the acquired predetermined data and searching the regional pattern models generated by the regional pattern model generating means for a regional pattern model with recognition performance suitable for recognizing the predetermined data on the new object based on the calculated likelihood.

19. The data process unit according to claim 18, wherein the mathematical distance calculating means uses the occurrence frequency of each pattern unit in the plurality of predetermined data on the plurality of objects when calculating the mathematical distance.

20. The data process unit according to claim 19, wherein the occurrence frequency of the pattern units is the occurrence frequency of pattern units whose recognition performance is lower than a predetermined threshold from among a plurality of pattern units related to the plurality of objects in pattern recognition of the plurality of predetermined data on the plurality of objects using the pattern models.

21. The data process unit according to any of claims 18 to 20, wherein the region dividing means divides the plurality of low dimensional vectors corresponding to pattern models by an outer circle and n inner circles (n is an integer equal to or larger than 1) and further divides ring-shaped regions formed by the concentric outer and inner circles therebetween into a plurality of regions by lines extending radially, where the outer circle is centered at the center of gravity of the coordinate points of all the low dimensional vectors corresponding to pattern models and has a radius equal to the distance between the center of gravity and the coordinate point of the low dimensional vector corresponding to pattern model farthest from the center of gravity while the inner circle is centered at the center of gravity and has a radius smaller than that of the outer circle.

22. The data process unit according to claim 21, wherein the region dividing means divides the low dimensional vectors corresponding to pattern models more finely with increasing radial distance from the innermost circle.

23. The data process unit according to claim 21 or 22, wherein the region dividing means divides regions in such a way that there will be partial overlap between low dimensional vectors corresponding to pattern models in adjacent regions.

24. The data process unit according to any of claims 21 to 23, wherein the regional pattern model searching means can calculate likelihood of regional pattern models for respective segment regions in relation to the acquired predetermined data beginning with the inner most circle and moving to the outer most circle and search the regional pattern models generated by the regional pattern model generating means for a regional pattern model with recognition performance suitable for recognizing the predetermined data on the new object based on the calculated likelihood.

25. The data process unit according to claim 24, wherein the regional pattern model searching means calculates the likelihood of the regional pattern model corresponding to the region within the innermost circle in relation to the acquired predetermined data, calculates the likelihoods of regional pattern models corresponding to the segment regions in the ring-shaped region just outside the innermost circle, calculates the likelihoods of the regional pattern models corresponding to the regions just outside the region containing the regional pattern model with the highest likelihood among the next inner regions, and subsequently goes on to calculate likelihoods in this manner moving outward in sequence.

26. The data process unit according to claim 24, wherein the regional pattern model searching means calculates the likelihood of the regional pattern model corresponding to the region within the innermost circle in relation to the acquired predetermined data, calculates the likelihoods of regional pattern models corresponding to the segment regions in the ring-shaped region just outside the innermost circle, calculates the likelihoods of the regional pattern models corresponding to the regions just outside the regions containing the regional pattern models with the top m likelihoods (m is an integer larger than 1) among the next inner regions, and subsequently goes on to calculate likelihoods in this manner moving outward in sequence.

27. The data process unit according to any of claims 18 to 26, wherein:

the plurality of predetermined data on a plurality of objects are data on a plurality of speech sounds produced by a plurality of speakers; and

the plurality of specific conditions include at least, the type of speaker, speech lexicons, speech styles, and speech environment.

28. The data process unit according to any of claims 18 to 27, wherein the data classification means forms a group of predetermined data under each combination of specific conditions selected freely from the plurality of specific conditions.

29. The data process unit according to any of claims 18 to 28, wherein the pattern models are generated using HMMs (Hidden Markov Models).

30. The data process unit according to claim 29, wherein the mathematical distance calculating means calculates the mathematical distance using one of Euclidean distance determined based on a mean vector of normal distributions of the pattern models generated using the HMMs, Euclidean distance determined based on a mean vector of normal distributions of the pattern models normalized by the standard deviation of the normal distributions of the pattern models, and Bhattacharrya distance determined based on normal distributions of the pattern models.

31. The data process unit according to any of claims 18 to 30, wherein the pattern model converting means converts the pattern models into the low dimensional vectors corresponding to the pattern models using Sammon'"'"'s method.

32. The data process unit according to any of claims 18 to 31, comprising division changing means for changing the results of divisions produced automatically by the region dividing means.

33. The data process unit according to any of claims 18 to 32, comprising pattern model adapting means for adapting the regional pattern model found by the pattern model searching means to the new object based on the predetermined data on the new object.

34. The data process unit according to claim 33, wherein:

the pattern model converting means can convert a high dimensional pattern model corresponding to the plurality of low dimensional vectors corresponding to the pattern models contained in the region corresponding to the regional pattern model found by the pattern model searching means into the low dimensional vectors corresponding to the pattern models;

the low dimensional vector corresponding to pattern model display means can display the plurality of low dimensional vectors corresponding to pattern models after the conversion as coordinate points in a low dimensional space of the same dimension as the low dimensional vectors corresponding to pattern models while maintaining the distance relationship based on values of low dimensional elements; and

the region dividing means can automatically divide the coordinate points of the plurality of low dimensional vectors corresponding to pattern models displayed in the low dimensional space into a plurality of regions in the low dimensional space.

36. A data process unit control program which is a computer-executable program for controlling the data process unit according to claim 18, comprising:

a data classification step of classifying a plurality of predetermined data on a plurality of objects into a plurality of groups based on a plurality of specific conditions;

a pattern model generating step of generating a plurality of pattern models which have 4-dimensional or higher dimensional elements for each group of predetermined data based on the predetermined data classified in the data classification step;

a mathematical distance calculating step of calculating mathematical distance among the pattern models generated in the pattern model generating step of the respective groups;

a pattern model converting step of converting the plurality of pattern models into the same number of low dimensional vectors corresponding to the pattern models in the lower dimension while maintaining distance relationship among the pattern models, based on the mathematical distance calculated in the mathematical distance calculating step;

a low dimensional vector corresponding to pattern model display step of displaying the plurality of low dimensional vectors corresponding to pattern models as coordinate points in a low dimensional space of the same dimension as the low dimensional vectors corresponding to pattern models while maintaining the distance relationship, based on values of low dimensional elements;

a region dividing step of automatically dividing the coordinate points of the plurality of low dimensional vectors corresponding to pattern models displayed in the low dimensional space in the low dimensional vector corresponding to pattern model display step into a plurality of regions in the low dimensional space;

a regional pattern model generating step of generating pattern models for each region based on predetermined data corresponding to coordinate points of the low dimensional vectors corresponding to pattern models contained in the segment regions;

a predetermined-data acquiring step of acquiring predetermined data on a new object; and

a regional pattern model searching step of calculating likelihood of regional pattern models for respective segment regions in relation to the acquired predetermined data and searching for a regional pattern model with recognition performance suitable for recognizing the predetermined data on the new object based on the calculated likelihood.

37. A specific pattern model providing system comprising:

an information processing terminal which is under the control of a system user; and

the data process unit according to any of claims 18 to 34,

wherein the information processing terminal and the data process unit are communicably connected with each other,

in the data process unit, the plurality of predetermined data on a plurality of objects are data on a plurality of speech sounds produced by a plurality of speakers,

the information processing terminal comprises speech data sending means for acquiring data on speech sounds produced by the system user and sending the acquired speech data to the data process unit, and specific pattern model acquiring means for acquiring the specific pattern models suitable for pattern recognition of the speech data of the system user from the data process unit,

the data process unit makes the predetermined-data acquiring means acquire the speech data from the information processing terminal and generates the specific pattern models for the system user based on the acquired speech data, and

the data process unit further comprises specific pattern model sending means for sending the generated specific pattern models to the information processing terminal.

38. A data process unit comprising:

acoustic space storing means for storing an acoustic space composed of a plurality of pattern models generated from speech data of a plurality of speakers;

speech data acquiring means for acquiring speech data of a target speaker;

position calculating means for calculating position of the speech data of the target speaker in the acoustic space based on the speech data of the target speaker acquired by the speech data acquiring means and the plurality of pattern models in the acoustic space stored by the acoustic space storing means;

speech data evaluating means for evaluating value of the speech data of the target speaker based on the position calculated by the position calculating means;

evaluation result display means for displaying evaluation results produced by the speech data evaluating means; and

positional relationship information display means for displaying information about positional relationship between the speech data and pattern models around the speech data in the acoustic space based on the calculated position.

39. The data process unit according to claim 38, wherein the speech data evaluating means evaluates the value of the speech data based on the number of pattern models existing within a predetermined distance from the position of the speech data of the target speaker calculated by the position calculating means.

40. The data process unit according to claim 39, wherein:

the predetermined distance is set stepwise; and

the speech data evaluating means evaluates the value of the speech data based on the number of pattern models existing within each distance range set stepwise.

41. The data process unit according to any of claims 38 to 40, wherein the speech data evaluating means uses a pattern model similar in features to the speech data of the target speaker out of the plurality of pattern models as the pattern model of the target speaker for the evaluation based on the position calculated by the position calculating means.

42. The data process unit according to claim 41, wherein the speech data evaluating means uses the top few pattern models similar in features to the speech data of the target speaker as the pattern models of the target speaker for the evaluation out of the plurality of pattern models.

43. The data process unit according to claim 41 or 42, wherein the position calculating means converts the speech data acquired by the speech data acquiring means into high dimensional feature data, calculates likelihood of match between the feature data and each of the plurality of the pattern models of the plurality of speakers, selects a specific pattern model from the plurality of pattern models of the plurality of speakers based on the calculated likelihood, calculates mathematical distance between the selected specific pattern model and other pattern models, and calculates the position of the acquired speech data in the acoustic space based on the calculated mathematical distance.

44. The data process unit according to any of claims 38 to 40, wherein the position calculating means converts the speech data acquired by the speech data acquiring means into high dimensional feature data, generates a pattern model of the target speaker based on the feature data, calculates mathematical distance between the generated pattern model and the plurality of pattern models of the plurality of speakers, and calculates the position of the acquired speech data in the acoustic space based on the calculated mathematical distance.

45. The data process unit according to claim 43 or 44, wherein:

the pattern models consist of 4-dimensional or higher dimensional elements; and

the positional relationship information display means converts a plurality of pattern models in the acoustic space including a plurality of pattern models corresponding to speech data of the target speaker into lower dimensional pattern models while maintaining the distance relationship and displays the pattern models after the conversion as coordinate points in a low dimensional space.

46. The data process unit according to any of claims 38 to 45, wherein the pattern models are generated using HMMs (Hidden Markov Models).

47. The data process unit according to any of claims 38 to 46, wherein:

the speech data evaluating means evaluates value of speech data of the target speaker on a phoneme-by-phoneme basis; and

the evaluation result display means displays evaluation results of the speech data of the target speaker on a phoneme-by-phoneme basis.

48. The data process unit according to any of claims 38 to 47, wherein the evaluation result display means displays supplementary information of the speech data when the speech data evaluating means evaluates the speech data of the target speaker as having low value.

49. The data process unit according to any of claims 38 to 48, comprising:

negotiating means for negotiating with the target speaker on whether the speech data can be provided; and

speech data storing means for storing the speech data over which negotiations are completed successfully by the negotiating means.

50. A data process system comprising:

an information processing terminal which is under the control of a target speaker; and

the data process unit according to any of claims 38 to 49, wherein;

the information processing terminal and the data process unit are communicably connected with each other,

the information processing terminal comprises speech data sending means for acquiring speech data of the target speaker and sending the acquired speech data to the data process unit, and evaluation information display means for displaying information about evaluation results of the speech data of the target speaker acquired from the data process unit, and

the data process unit comprises evaluation information sending means for sending the information about the evaluation results to the information processing terminal.

52. A data process unit control program which is a computer-executable program for controlling the data process unit according to claim 38, comprising:

an acoustic space storing step of storing an acoustic space composed of a plurality of pattern models generated from speech data of a plurality of speakers;

a speech data acquiring step of acquiring speech data of a target speaker;

a position calculating step of calculating position of the speech data of the target speaker in the acoustic space based on the speech data acquired in the speech data acquiring step and the plurality of pattern models in the acoustic space stored in the acoustic space storing step;

a speech data evaluating step of evaluating value of the speech data of the target speaker based on the position calculated in the position calculating step; and

an evaluation result display step of displaying evaluation results produced in the speech data evaluating step.

53. The data process unit applicable to the data process system according to claim 50, comprising:

acoustic space storing means for storing an acoustic space composed of a plurality of pattern models generated from speech data of a plurality of speakers;

speech data acquiring means for acquiring speech data of a target speaker;

position calculating means for calculating position of the speech data of the target speaker in the acoustic space based on the speech data acquired by the speech data acquiring means and the plurality of pattern models in the acoustic space stored by the acoustic space storing means;

speech data evaluating means for evaluating value of the speech data of the target speaker based on the position calculated by the position calculating means;

evaluation result display means for displaying evaluation results produced by the speech data evaluating means;

positional relationship information display means for displaying information about positional relationship between the speech data and pattern models around the speech data in the acoustic space based on the calculated position; and

evaluation information sending means for sending the information about the evaluation results to the information processing terminal.

54. An information processing terminal applicable to the data process system according to claim 50, comprising:

speech data sending means for acquiring speech data of the target speaker and sending the acquired speech data to the data process unit; and

evaluation information display means for displaying information about evaluation results of the speech data of the target speaker acquired from the data process unit.

55. A data process unit control program which is a computer-executable program for controlling the data process unit according to claim 53,

wherein the data process unit comprises an acoustic space composed of a plurality of pattern models generated from speech data of a plurality of speakers,

the program comprising:

a speech data acquiring step of acquiring speech data of a target speaker;

a position calculating step of calculating position of the speech data of the target speaker in the acoustic space based on the speech data acquired in the speech data acquiring step and the plurality of pattern models in the acoustic space;

a speech data evaluating step of evaluating value of the speech data of the target speaker based on the position calculated in the position calculating step;

an evaluation result display step of displaying evaluation results produced in the speech data evaluating step;

a positional relationship information display step of displaying information about positional relationship between the speech data and pattern models around the speech data in the acoustic space based on the calculated position; and

an evaluation information sending step of sending the information about the evaluation results to the information processing terminal.

56. An information processing terminal control program which is a computer-executable program for controlling the information processing terminal according to claim 54, comprising:

a speech data sending step of acquiring speech data of the target speaker and sending the acquired speech data to the data process unit; and

an evaluation information display step of displaying information about evaluation results of speech data of the target speaker acquired from the data process unit.

51. A data process method comprising the steps of:

preparing an acoustic space composed of a plurality of pattern models generated from speech data of a plurality of speakers;

acquiring speech data of a target speaker;

calculating position of the speech data of the target speaker in the acoustic space based on the acquired speech data and the plurality of pattern models in the acoustic space;

evaluating value of the speech data of the target speaker based on the calculated position; and

displaying the evaluation results.

57. A data process unit comprising:

acoustic space storing means for storing an acoustic space composed of a plurality of pattern models generated from speech data of a plurality of speakers;

speech data acquiring means for acquiring speech data of a target speaker;

position calculating means for calculating position of the speech data of the target speaker in the acoustic space based on the speech data of the target speaker and the plurality of pattern models in the acoustic space;

similar-speaker detecting means for detecting similar speakers who resemble the target speaker in speech out of the plurality of speakers based on the position of the speech data and the plurality of pattern models; and

positional relationship information display means for displaying information about positional relationship between the speech data of the target speaker and pattern models of the similar speakers in the acoustic space based on the position of the speech data and the pattern models of the similar speakers.

58. The data process unit according to claim 57, comprising:

specific speaker specifying means for specifying a specific speaker among the plurality of speakers;

similarity evaluating means for evaluating similarity in speech between the specific speaker and the target speaker based on the position of the speech data and the pattern model of the specific speaker in the acoustic space; and

evaluation result display means for displaying evaluation results produced bythe similarity evaluating means, wherein

the positional relationship information display means displays information about positional relationship between the speech data of the target speaker and pattern model of the specific speaker in the acoustic space based on the position of the speech data and the pattern model of the specific speaker.

60. The data process unit according to claim 58 or 59, comprising:

correction information generating means for generating correction information which indicates corrections to be made to the speech of the target speaker in order to enhance similarity in speech between the target speaker and the specific speaker based on the evaluation results produced by the similarity evaluating means; and

correction information display means for displaying the correction information.

64. The data process unit according to any of claims 57 to 60, wherein the position calculating means converts the speech data acquired by the speech data acquiring means into high dimensional feature data, generates a pattern model of the target speaker based on the feature data, calculates mathematical distance between the generated pattern model and the plurality of pattern models of the plurality of speakers, and calculates the position of the acquired speech data in the acoustic space based on the calculated mathematical distance.

66. The data process unit according to any of claims 58 to 65, wherein the similarity evaluating means evaluates the similarity of the speech data of the target speaker on a phoneme-by-phoneme basis.

67. The data process unit according to any of claims 58 to 66, wherein:

the acoustic space is composed of a plurality of pattern models generated from speech data of the plurality of speakers in a plurality of speech styles; and

the similarity evaluating means evaluates the similarity in each of the plurality of speech styles.

68. The data process unit according to claim 67, wherein the positional relationship information display means establishes a coordinate axis of the low dimensional space based on the speech styles for the plurality of pattern models.

69. The data process unit according to any of claims 57 to 68, wherein the pattern models are generated using HMMs (Hidden Markov Models).

70. A data process system comprising:

an information processing terminal which is under the control of a target speaker; and

the data process unit according to any of claims 57 to 69, wherein;

the information processing terminal and the data process unit are communicably connected with each other, the information processing terminal comprises speech data sending means for acquiring speech data of the target speaker and sending the acquired speech data to the data process unit, and information display means for displaying information about processing results of speech data acquired from the data process unit, and

the data process unit comprises information sending means for sending the information about the processing results of the speech data to the information processing terminal.

59. A data process unit comprising:

acoustic space storing means for storing an acoustic space composed of a plurality of pattern models generated from speech data of a plurality of speakers;

specific speaker specifying means for specifying a specific speaker among the plurality of speakers;

speech data acquiring means for acquiring speech data of a target speaker;

position calculating means for calculating position of the speech data of the target speaker based on the speech data of the target speaker and the plurality of pattern models in the acoustic space;

similarity evaluating means for evaluating similarity in speech between the specific speaker and the target speaker based on the position of the speech data and the pattern model of the specific speaker;

evaluation result display means for displaying evaluation results produced by the similarity evaluating means; and

positional relationship information display means for displaying information about positional relationship between the speech data of the target speaker and pattern model of the specific speaker in the acoustic space based on the position of the speech data and the pattern model of the specific speaker.

61. The data process unit according to claim 59, wherein:

the similar-speaker detecting means uses a pattern model which is similar in features to the speech data of the target speaker out of the plurality of pattern models as the pattern model of the target speaker based on the position of the speech data; and

the similarity evaluating means uses a pattern model which is similar in features to the speech data of the target speaker out of the plurality of pattern models as the pattern model of the target speaker based on the position of the speech data.

62. The data process unit according to claim 59, wherein:

the similar-speaker detecting means uses the top few pattern models which are similar in features to the speech data of the target speaker out of the plurality of pattern models as the pattern model of the target speaker based on the position of the speech data; and

the similarity evaluating means uses the top few pattern models which are similar in features to the speech data of the target speaker out of the plurality of pattern models as the pattern model of the target speaker based on the position of the speech data.

63. The data process unit according to claims 61 or 62, wherein the position calculating means converts the speech data acquired by the speech data acquiring means into high dimensional feature data, calculates likelihood between the feature data and each of the plurality of the pattern models of the plurality of speakers, selects a specific pattern model from the pattern models of the plurality of speakers based on the calculated likelihood, calculates mathematical distance between the selected specific pattern model and other pattern models, and calculates the position of the acquired speech data in the acoustic space based on the calculated mathematical distance.

65. The data process unit according to claim 63 or 64, wherein:

the pattern models consist of 4-dimensional or higher dimensional elements; and

the positional relationship information display means converts a plurality of pattern models in the acoustic space including a plurality of pattern models corresponding to speech data of the target speaker into lower dimensional pattern models while maintaining the distance relationship and displays the pattern models after the conversion as coordinate points in a low dimensional space.

71. A data process method comprising the steps of:

preparing an acoustic space composed of a plurality of pattern models generated from speech data of a plurality of speakers;

acquiring speech data of a target speaker;

calculating position of the speech data of the target speaker in the acoustic space based on the speech data of the target speaker and the plurality of pattern models in the acoustic space;

detecting similar speakers who resemble the target speaker in speech out of the plurality of speakers based on the position of the speech data and the plurality of pattern models; and

displaying information about positional relationship between the speech data of the target speaker and pattern models of the similar speakers in the acoustic space based on the position of the speech data and the pattern models of the similar speakers.

72. The data process method according to claim 71, comprising the steps of:

specifying a specific speaker among the plurality of speakers;

evaluating similarity in speech between the specific speaker and the target speaker based on the position of the speech data and the pattern model of the specific speaker in the acoustic space; and

displaying the evaluation results.

73. A data process unit comprising the steps of:

preparing an acoustic space composed of a plurality of pattern models generated from speech data of a plurality of speakers;

specifying a specific speaker among the plurality of speakers;

acquiring speech data of a target speaker;

calculating position of the speech data of the target speaker based on the speech data of the target speaker and the plurality of pattern models in the acoustic space;

evaluating similarity in speech between the specific speaker and the target speaker based on the position of the speech data and the pattern model of the specific speaker;

displaying evaluation results; and

displaying information about positional relationship between the speech data of the target speaker and pattern model of the specific speaker in the acoustic space based on the position of the speech data and the pattern model of the specific speaker.

74. A data process unit control program comprising:

an acoustic space storing step of storing an acoustic space composed of a plurality of pattern models generated from speech data of a plurality of speakers;

a speech data acquiring step of acquiring speech data of a target speaker;

a position calculating step of calculating position of the speech data of the target speaker in the acoustic space based on the speech data of the target speaker and the plurality of pattern models in the acoustic space;

a similar-speaker detecting step of detecting similar speakers who resemble the target speaker in speech out of the plurality of speakers based on the position of the speech data and the plurality of pattern models;

a positional relationship information display step of displaying information about positional relationship between the speech data of the target speaker and pattern models of the similar speakers in the acoustic space based on the position of the speech data and the pattern models of the similar speakers;

a speaker specifying step of specifying a specific speaker;

a similarity evaluating step of evaluating similarity in speech between the specific speaker and the target speaker based on the position of the speech data and the pattern model of the specific speaker in the acoustic space; and

an evaluation result display step of displaying evaluation results produced by the similarity evaluating step, wherein

the positional relationship information display step displays information about positional relationship between the speech data of the target speaker and pattern model of the specific speaker in the acoustic space based on the position of the speech data and the pattern model of the specific speaker.

75. A data process unit control program comprising:

an acoustic space storing step of storing an acoustic space composed of a plurality of pattern models generated from speech data of a plurality of speakers;

a specific speaker specifying step of specifying a specific speaker among the plurality of speakers;

a speech data acquiring step of acquiring speech data of a target speaker;

a position calculating step of calculating position of the speech data of the target speaker based on the speech data of the target speaker and the plurality of pattern models in the acoustic space;

a similarity evaluating step of evaluating similarity in speech between the specific speaker and the target speaker based on the position of the speech data and the pattern model of the specific speaker;

an evaluation result display step of displaying evaluation results produced by the similarity evaluating step; and

a positional relationship information display step of displaying information about positional relationship between the speech data of the target speaker and pattern model of the specific speaker in the acoustic space based on the position of the speech data and the pattern model of the specific speaker.