1. An Elitist Approach to Articulatory-Acoustic Feature Classification in English and in Dutch Steven Greenberg, Shawn Chang and Mirjam Wester International Computer Science Institute 1947 Center Street, Berkeley, CA 94704 http://www.icsi.berkeley.edu/~steveng steveng@icsi.berkeley.edu http://www.icsi.berkeley.edu/~shawnc shawnc@icsi.berkeley.edu Mirjam Wester A 2 RT, Department of Language and Speech Nijmegen University, Netherlands http://www.lands.let.kun.nl/Tspublic/wester wester@let.kun.nl

2. Acknowledgements and Thanks Automatic Feature Classification and Analysis Joy Hollenback, Lokendra Shastri, Rosaria Silipo Research Funding U.S. National Science Foundation U.S. Department of Defense

3. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech

4. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech –There are systematic patterns in “real” speech that potentially reveal underlying principles of linguistic organization

5. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech –There are systematic patterns in “real” speech that potentially reveal underlying principles of linguistic organization Phonetic and Prosodic Annotation Material is of Limited Quantity

6. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech –There are systematic patterns in “real” speech that potentially reveal underlying principles of linguistic organization Phonetic and Prosodic Annotation Material is of Limited Quantity –Phonetic and prosodic material important for understanding spoken language and developing superior technology for recognition and synthesis

7. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech –There are systematic patterns in “real” speech that potentially reveal underlying principles of linguistic organization Phonetic and Prosodic Annotation Material is of Limited Quantity –Phonetic and prosodic material important for understanding spoken language and developing superior technology for recognition and synthesis Manual Annotation of Phonetic and Prosodic Material is a Pain in the Butt to Produce

8. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech –There are systematic patterns in “real” speech that potentially reveal underlying principles of linguistic organization Phonetic and Prosodic Annotation Material is of Limited Quantity –Phonetic and prosodic material important for understanding spoken language and developing superior technology for recognition and synthesis Manual Annotation of Phonetic and Prosodic Material is a Pain in the Butt to Produce –Hand labeling and segmentation is time consuming and expensive

9. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech –There are systematic patterns in “real” speech that potentially reveal underlying principles of linguistic organization Phonetic and Prosodic Annotation Material is of Limited Quantity –Phonetic and prosodic material important for understanding spoken language and developing superior technology for recognition and synthesis Manual Annotation of Phonetic and Prosodic Material is a Pain in the Butt to Produce –Hand labeling and segmentation is time consuming and expensive –It is difficult to find qualified transcribers and training can be arduous

10. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech –There are systematic patterns in “real” speech that potentially reveal underlying principles of linguistic organization Phonetic and Prosodic Annotation Material is of Limited Quantity –Phonetic and prosodic material important for understanding spoken language and developing superior technology for recognition and synthesis Manual Annotation of Phonetic and Prosodic Material is a Pain in the Butt to Produce –Hand labeling and segmentation is time consuming and expensive –It is difficult to find qualified transcribers and training can be arduous Automatic Alignment Systems (used in speech recognition) are Inaccurate both in Terms of Labeling and Segmentation

11. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech –There are systematic patterns in “real” speech that potentially reveal underlying principles of linguistic organization Phonetic and Prosodic Annotation Material is of Limited Quantity –Phonetic and prosodic material important for understanding spoken language and developing superior technology for recognition and synthesis Manual Annotation of Phonetic and Prosodic Material is a Pain in the Butt to Produce –Hand labeling and segmentation is time consuming and expensive –It is difficult to find qualified transcribers and training can be arduous Automatic Alignment Systems (used in speech recognition) are Inaccurate both in Terms of Labeling and Segmentation –Forced-alignment-based segmentation is poor - ca. 40% off on phone boundaries

12. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech –There are systematic patterns in “real” speech that potentially reveal underlying principles of linguistic organization Phonetic and Prosodic Annotation Material is of Limited Quantity –Phonetic and prosodic material important for understanding spoken language and developing superior technology for recognition and synthesis Manual Annotation of Phonetic and Prosodic Material is a Pain in the Butt to Produce –Hand labeling and segmentation is time consuming and expensive –It is difficult to find qualified transcribers and training can be arduous Automatic Alignment Systems (used in speech recognition) are Inaccurate both in Terms of Labeling and Segmentation –Forced-alignment-based segmentation is poor - ca. 40% off on phone boundaries –Phone classification error is ca. 30-50%

13. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech –There are systematic patterns in “real” speech that potentially reveal underlying principles of linguistic organization Phonetic and Prosodic Annotation Material is of Limited Quantity –Phonetic and prosodic material important for understanding spoken language and developing superior technology for recognition and synthesis Manual Annotation of Phonetic and Prosodic Material is a Pain in the Butt to Produce –Hand labeling and segmentation is time consuming and expensive –It is difficult to find qualified transcribers and training can be arduous Automatic Alignment Systems (used in speech recognition) are Inaccurate both in Terms of Labeling and Segmentation –Forced-alignment-based segmentation is poor - ca. 40% off on phone boundaries –Phone classification error is ca. 30-50% –Speech recognition systems do not currently deal with prosody

14. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech –There are systematic patterns in “real” speech that potentially reveal underlying principles of linguistic organization Phonetic and Prosodic Annotation Material is of Limited Quantity –Phonetic and prosodic material important for understanding spoken language and developing superior technology for recognition and synthesis Manual Annotation of Phonetic and Prosodic Material is a Pain in the Butt to Produce –Hand labeling and segmentation is time consuming and expensive –It is difficult to find qualified transcribers and training can be arduous Automatic Alignment Systems (used in speech recognition) are Inaccurate both in Terms of Labeling and Segmentation –Forced-alignment-based segmentation is poor - ca. 40% off on phone boundaries –Phone classification error is ca. 30-50% –Speech recognition systems do not currently deal with prosody Automatic Transcription is Likely to Aid in the Development of Speech Recognition and Synthesis Technology

15. Motivation for Automatic Transcription Many Properties of Spontaneous Spoken Language Differ from Those of Laboratory and Citation Speech –There are systematic patterns in “real” speech that potentially reveal underlying principles of linguistic organization Phonetic and Prosodic Annotation Material is of Limited Quantity –Phonetic and prosodic material important for understanding spoken language and developing superior technology for recognition and synthesis Manual Annotation of Phonetic and Prosodic Material is a Pain in the Butt to Produce –Hand labeling and segmentation is time consuming and expensive –It is difficult to find qualified transcribers and training can be arduous Automatic Alignment Systems (used in speech recognition) are Inaccurate both in Terms of Labeling and Segmentation –Forced-alignment-based segmentation is poor - ca. 40% off on phone boundaries –Phone classification error is ca. 30-50% –Speech recognition systems do not currently deal with prosody Automatic Transcription is Likely to Aid in the Development of Speech Recognition and Synthesis Technology –And therefore is worth the effort to develop

16. Road Map of the Presentation Introduction –Motivation for developing automatic phonetic transcription systems –Rationale for the current focus on articulatory-acoustic features (AFs) –The development corpus - NTIMIT –Justification for using NTIMIT for development of AF classifiers

17. Road Map of the Presentation Introduction –Motivation for developing automatic phonetic transcription systems –Rationale for the current focus on articulatory-acoustic features (AFs) –The development corpus - NTIMIT –Justification for using NTIMIT for development of AF classifiers The ELITIST Approach and Its Application to English –The baseline system –The ELITIST approach –Manner-specific classification for place of articulation features

18. Road Map of the Presentation Introduction –Motivation for developing automatic phonetic transcription systems –Rationale for the current focus on articulatory-acoustic features (AFs) –The development corpus - NTIMIT –Justification for using NTIMIT for development of AF classifiers The ELITIST Approach and Its Application to English –The baseline system –The ELITIST approach –Manner-specific classification for place of articulation features Application of the ELITIST Approach to Dutch –The training and testing corpus - VIOS –The nature of cross-linguistic transfer of articulatory-acoustic features –The ELITIST approach to frame selection as applied to the VIOS corpus –Improvement of place-of-articulation classification using manner-specific training in Dutch

19. Road Map of the Presentation Introduction –Motivation for developing automatic phonetic transcription systems –Rationale for the current focus on articulatory-acoustic features (AFs) –The development corpus - NTIMIT –Justification for using NTIMIT for development of AF classifiers The ELITIST Approach and Its Application to English –The baseline system –The ELITIST approach –Manner-specific classification for place of articulation features Application of the ELITIST Approach to Dutch –The training and testing corpus - VIOS –The nature of cross-linguistic transfer of articulatory-acoustic features –The ELITIST approach to frame selection as applied to the VIOS corpus –Improvement of place-of-articulation classification using manner-specific training in Dutch Conclusions and Future Work –Development of fully automatic phonetic and prosodic transcription systems –An empirically oriented discipline based on annotated corpora

20. Part One INTRODUCTION Motivation for Developing Automatic Phonetic Transcription Systems Rationale for the Current Focus on Articulatory-Acoustic Features Description of the Development Corpus – NTIMIT Justification for Using the NTIMIT Corpus

21. Provides Detailed, Empirical Material for the Study of Spoken Language –Such data provide an important basis for scientific insight and understanding –Facilitates development of new models for spoken language Corpus Generation - Objectives

22. Provides Detailed, Empirical Material for the Study of Spoken Language –Such data provide an important basis for scientific insight and understanding –Facilitates development of new models for spoken language Provides Training Material for Technology Applications –Automatic speech recognition, particularly pronunciation models –Speech synthesis, ditto –Cross-linguistic transfer of technology algorithms Corpus Generation - Objectives

23. Provides Detailed, Empirical Material for the Study of Spoken Language –Such data provide an important basis for scientific insight and understanding –Facilitates development of new models for spoken language Provides Training Material for Technology Applications –Automatic speech recognition, particularly pronunciation models –Speech synthesis, ditto –Cross-linguistic transfer of technology algorithms Promotes Development of NOVEL Algorithms for Speech Technology –Pronunciation models and lexical representations for automatic speech recognition speech synthesis –Multi-tier representations of spoken language Corpus Generation - Objectives

24. Corpus-Centric View of Spoken Language Our Focus in Today’s Presentation is on Articulatory Feature Classification Other levels of linguistic representation are also extremely important to annotate Our Focus

25. Rationale for Articulatory-Acoustic Features Articulatory-Acoustic Features (AFs) are the “Building Blocks” of the Lowest (i.e., Phonetic) Tier of Spoken Language –AFs can be combined in a variety of ways to specify virtually any speech sound found in the world’s languages

26. Rationale for Articulatory-Acoustic Features Articulatory-Acoustic Features (AFs) are the “Building Blocks” of the Lowest (i.e., Phonetic) Tier of Spoken Language –AFs can be combined in a variety of ways to specify virtually any speech sound found in the world’s languages –AFs are therefore more appropriate for cross-linguistic transfer than phonetic segments

27. Rationale for Articulatory-Acoustic Features Articulatory-Acoustic Features (AFs) are the “Building Blocks” of the Lowest (i.e., Phonetic) Tier of Spoken Language –AFs can be combined in a variety of ways to specify virtually any speech sound found in the world’s languages –AFs are therefore more appropriate for cross-linguistic transfer than phonetic segments AFs are Systematically Organized at the Level of the Syllable –Syllables are a basic articulatory unit in speech

28. Rationale for Articulatory-Acoustic Features Articulatory-Acoustic Features (AFs) are the “Building Blocks” of the Lowest (i.e., Phonetic) Tier of Spoken Language –AFs can be combined in a variety of ways to specify virtually any speech sound found in the world’s languages –AFs are therefore more appropriate for cross-linguistic transfer than phonetic segments AFs are Systematically Organized at the Level of the Syllable –Syllables are a basic articulatory unit in speech –The pronunciation patterns observed in casual conversation are systematic at the AF level, but not at the phonetic-segment level, and therefore can be used to develop more accurate and flexible pronunciation models than phonetic segments

29. Rationale for Articulatory-Acoustic Features Articulatory-Acoustic Features (AFs) are the “Building Blocks” of the Lowest (i.e., phonetic) Tier of Spoken Language –AFs can be combined in a multitude of ways to specify virtually any speech sound found in the world’s languages –AFs are therefore more appropriate for cross-linguistic transfer than phonetic segments AFs are Systematically Organized at the Level of the Syllable –Syllables are a basic articulatory unit in speech –The pronunciation patterns observed in casual conversation are systematic at the AF level, but not at the phonetic-segment level, and therefore can be used to develop more accurate and flexible pronunciation models than phonetic segments AFs are Potentially More Effective in Speech Recognition Systems –More accurate and flexible pronunciation models (tied to syllabic and lexical units)

30. Rationale for Articulatory-Acoustic Features Articulatory-Acoustic Features (AFs) are the “Building Blocks” of the Lowest (i.e., phonetic) Tier of Spoken Language –AFs can be combined in a multitude of ways to specify virtually any speech sound found in the world’s languages –AFs are therefore more appropriate for cross-linguistic transfer than phonetic segments AFs are Systematically Organized at the Level of the Syllable –Syllables are a basic articulatory unit in speech –The pronunciation patterns observed in casual conversation are systematic at the AF level, but not at the phonetic-segment level, and therefore can be used to develop more accurate and flexible pronunciation models than phonetic segments AFs are Potentially More Effective in Speech Recognition Systems –More accurate and flexible pronunciation models (tied to syllabic and lexical units) –Are generally more robust under acoustic interference than phonetic segments

31. Rationale for Articulatory-Acoustic Features Articulatory-Acoustic Features (AFs) are the “Building Blocks” of the Lowest (i.e., phonetic) Tier of Spoken Language –AFs can be combined in a multitude of ways to specify virtually any speech sound found in the world’s languages –AFs are therefore more appropriate for cross-linguistic transfer than phonetic segments AFs are Systematically Organized at the Level of the Syllable –Syllables are a basic articulatory unit in speech –The pronunciation patterns observed in casual conversation are systematic at the AF level, but not at the phonetic-segment level, and therefore can be used to develop more accurate and flexible pronunciation models than phonetic segments AFs are Potentially More Effective in Speech Recognition Systems –More accurate and flexible pronunciation models (tied to syllabic and lexical units) –Are generally more robust under acoustic interference than phonetic segments –Relatively few alternative features for various AF dimensions makes classification inherently more robust than phonetic segments

32. Rationale for Articulatory-Acoustic Features Articulatory-Acoustic Features (AFs) are the “Building Blocks” of the Lowest (i.e., phonetic) Tier of Spoken Language –AFs can be combined in a multitude of ways to specify virtually any speech sound found in the world’s languages –AFs are therefore more appropriate for cross-linguistic transfer than phonetic segments AFs are Systematically Organized at the Level of the Syllable –Syllables are a basic articulatory unit in speech –The pronunciation patterns observed in casual conversation are systematic at the AF level, but not at the phonetic-segment level, and therefore can be used to develop more accurate and flexible pronunciation models than phonetic segments AFs are Potentially More Effective in Speech Recognition Systems –More accurate and flexible pronunciation models (tied to syllabic and lexical units) –Are generally more robust under acoustic interference than phonetic segments –Relatively few alternative features for various AF dimensions makes classification inherently more robust than phonetic segments AFs are Potentially More Effective in Speech Synthesis Systems –More accurate and flexible pronunciation models (tied to syllabic and lexical units)

33. Sentences Read by Native Speakers of American English –Quasi-phonetically balanced set of materials –Wide range of dialect variability, both genders, variation in speaker age –Relatively low semantic predictability “She washed his dark suit in greasy wash water all year” Primary Development Corpus – NTIMIT

34. Sentences Read by Native Speakers of American English –Quasi-phonetically balanced set of materials –Wide range of dialect variability, both genders, variation in speaker age –Relatively low semantic predictability “She washed his dark suit in greasy wash water all year” Corpus Manually Labeled and Segmented at the Phonetic-Segment Level –The precision of phonetic annotation provides an excellent training corpus –Corpus was annotated at MIT Primary Development Corpus – NTIMIT

35. Sentences Read by Native Speakers of American English –Quasi-phonetically balanced set of materials –Wide range of dialect variability, both genders, variation in speaker age –Relatively low semantic predictability “She washed his dark suit in greasy wash water all year” Corpus Manually Labeled and Segmented at the Phonetic-Segment Level –The precision of phonetic annotation provides an excellent training corpus –Corpus was annotated at MIT A Large Amount of Annotated Material –Over 2.5 hours of material used for training the classifiers –20 minutes of material used for testing Primary Development Corpus – NTIMIT

36. Sentences Read by Native Speakers of American English –Quasi-phonetically balanced set of materials –Wide range of dialect variability, both genders, variation in speaker age –Relatively low semantic predictability “She washed his dark suit in greasy wash water all year” Corpus Manually Labeled and Segmented at the Phonetic-Segment Level –The precision of phonetic annotation provides an excellent training corpus –Corpus was annotated at MIT A Large Amount of Annotated Material –Over 2.5 hours of material used for training the classifiers –20 minutes of material used for testing Relatively Canonical Pronunciation Ideal for Training AF Classifiers –Formal pronunciation patterns provides a means of deriving articulatory features from phonetic-segment labels via mapping rules (cf. Proceedings paper for details) Primary Development Corpus – NTIMIT

37. Sentences Read by Native Speakers of American English –Quasi-phonetically balanced set of materials –Wide range of dialect variability, both genders, variation in speaker age –Relatively low semantic predictability “She washed his dark suit in greasy wash water all year” Corpus Manually Labeled and Segmented at the Phonetic-Segment Level –The precision of phonetic annotation provides an excellent training corpus –Corpus was annotated at MIT A Large Amount of Annotated Material –Over 2.5 hours of material used for training the classifiers –20 minutes of material used for testing Relatively Canonical Pronunciation Ideal for Training AF Classifiers –Formal pronunciation patterns provides a means of deriving articulatory features from phonetic-segment labels via mapping rules (cf. Proceedings paper for details) NTIMIT is a Telephone Pass-band Version of the TIMIT Corpus –Sentential material passed through a channel between 0.3 and 3.4 kHz –Provides capability of transfer to other telephone corpora (such as VIOS) Primary Development Corpus – NTIMIT

38. Part Two THE ELITIST APPROACH The Baseline System for Articulatory-Acoustic Feature Classification The ELITIST Approach to Systematic Frame Selection for AF Classification Improving Place-of-Articulation Classification Using Manner-Specific Training

39. The Baseline System for AF Classification Spectro-Temporal Representation of the Speech Signal –Derived from logarithmically compressed, critical-band energy pattern –25-ms analysis windows (i.e., a frame) –10-ms frame-sampling interval (i.e., 60% overlap between adjacent frames)

40. The Baseline System for AF Classification Spectro-Temporal Representation of the Speech Signal –Derived from logarithmically compressed, critical-band energy pattern –25-ms analysis windows (i.e., a frame) –10-ms frame-sampling interval (i.e., 60% overlap between adjacent frames) Multilayer Perceptron (MLP) Neural Network Classifiers –Single hidden layer of 200-400 units, trained with back-propagation –Nine frames of context used in the input

41. The Baseline System for AF Classification An MLP Network for Each Articulatory Feature (AF) Dimension –A separate network trained on voicing, place and manner of articulation, etc. –Training targets were derived from hand-labeled phonetic transcripts and a fixed phone-to-AF mapping –“Silence” was a feature included in the classification of each AF dimension –All of the results reported are for FRAME accuracy (not segmental accuracy)

42. The Baseline System for AF Classification Focus on Articulatory Feature Classification Rather than Phone Identity –Provides a more accurate means of assessing MLP-based classification system

43. Baseline System Performance Summary Classification of Articulatory Features Exceeds 80% – Except for Place Objective – Improve Classification across All AF Dimensions, but Particularly on Place-of-Articulation NTIMIT Corpus

44. Correlation Between Frame Position and Classification Accuracy for MANNER of articulation features: –The 20% of the frames closest to the segment BOUNDARIES are 73% correct –The 20% of the frames closest to the segment CENTER are 90% correct Not All Frames are Created Equal

45. Correlation Between Frame Position and Classification Accuracy for MANNER of articulation features: –The 20% of the frames closest to the segment BOUNDARIES are 73% correct –The 20% of the frames closest to the segment CENTER are 90% correct Correlation between frame position within a segment and classifier output for MANNER features: –The 20% of the frames closest to the segment BOUNDARIES have a mean maximum output (“confidence”) level of 0.797 –The 20% of the frames closest to the segment CENTER have a mean maximum output (“confidence”) level of 0.892 –This dynamic range of 0.1 (in absolute terms) is HIGHLY significant Not All Frames are Created Equal

46. Manner Classification is Best for Frames in the Phonetic-Segment Center

47. Not All Frames are Created Equal Manner Classification is Best for Frames in the Phonetic-Segment Center MLP Network Confidence Level is Highly Correlated with Frame Accuracy

48. Not All Frames are Created Equal Manner Classification is Best for Frames in the Phonetic-Segment Center MLP Network Confidence Level is Highly Correlated with Frame Accuracy The Most Confidently Classified Frames are Generally More Accurate

49. Selecting a Threshold for Frame Selection The Correlation Between Neural Network Confidence Level and Frame Position within the Phonetic Segment Can Be Exploited to Enhance Articulatory Feature Classification –This insight provides the basis for the “Elitist” approach

50. Selecting a Threshold for Frame Selection The Most Confidently Classified Frames are Generally More Accurate

51. Selecting a Threshold for Frame Selection The Most Confidently Classified Frames are Generally More Accurate Frames with Confidence Levels Below “Threshold” are Discarded –Setting the threshold to 0.7 filters out ca. 20% of the frames –Boundary frames are twice as likely to be discarded as central frames Criterion

52. Selecting a Threshold for Frame Selection The Most Confidently Classified Frames are Generally More Accurate Frames with Confidence Levels Below “Threshold” are Discarded –Setting the threshold to 0.7 filters out ca. 20% of the frames –Boundary frames are twice as likely to be discarded as central frames Primary Drawback of Using This Threshold for Frame Selection –6% of the phonetic segments have most of their frames discarded Criterion

53. The Accuracy of MANNER Frame Classification Improves –Frame-level classification accuracy increases overall from 85% to 93% The Elitist Approach to Manner Classification

54. The Accuracy of MANNER Frame Classification Improves –Frame-level classification accuracy increases overall from 85% to 93% Certain Manner Classes Improve Highly with Frame Selection –Nasals, Stops, Fricatives, Flaps all show strong improvement in performance The Elitist Approach to Manner Classification

55. Objective – Reduce the Number of Place Features to Classify for Any Single Manner Class –Although there are NINE distinct place of articulation features overall... –For any single manner class there are only three or four place features –The specific PLACES of articulation for stops differs from fricatives, etc. –HOWEVER, the SPATIAL PATTERNING of the constriction loci is SIMILAR Manner-Dependency for Place of Articulation

56. Objective – Reduce the Number of Place Features to Classify for Any Single Manner Class –Although there are NINE distinct place of articulation features overall... –For any single manner class there are only three or four place features –The specific PLACES of articulation for stops differs from fricatives, etc. –HOWEVER, the SPATIAL PATTERNING of the constriction loci is SIMILAR Because Classification Accuracy for Manner Features is High, Manner- Specific Training for Place of Articulation is Feasible (as we’ll show you) Manner-Dependency for Place of Articulation

57. Manner-Specific Place Classification Thus, Each Manner Class can be Trained on Comparable Relational Place Features: ANTERIOR CENTRAL POSTERIOR

58. Manner-Specific Place Classification NTIMIT (telephone) Corpus Thus, Each Manner Class can be Trained on Comparable Relational Place Features: ANTERIOR CENTRAL POSTERIOR Classifying Place of Articulation in Manner-Specific Fashion Can Improve the Classification Accuracy of this Feature Dimension –The training material is far more homogeneous under this regime and is thus more reliable and robust

59. Manner-Specific Classification – Vowels Knowing the “Manner” Improves “Place” Classification for Vowels as Well Also Improves “Height” Classification NTIMIT (telephone) Corpus

60. Manner-Specific Place Classification - Overall NTIMIT (telephone) Corpus Overall, Performance Improves Between 5% and 14% (in absolute terms) Improvement is Greatest for Stops, Nasals and Flaps

61. A Principled Method of Frame Selection (the ELITIST approach) can be Used to Improve the Accuracy of Articulatory Feature Classification Summary – ELITIST Approach

62. A Principled Method of Frame Selection (the ELITIST approach) can be Used to Improve the Accuracy of Articulatory Feature Classification The ELITIST Approach is Based on the Observation that Frames In the Center of Phonetic Segments are More Accurately Classified than Those at Segment Boundaries Summary – ELITIST Approach

63. A Principled Method of Frame Selection (the ELITIST approach) can be Used to Improve the Accuracy of Articulatory Feature Classification The ELITIST Approach is Based on the Observation that Frames In the Center of Phonetic Segments are More Accurately Classified than Those at Segment Boundaries Frame Classification Accuracy is Highly Correlated with MLP Network Confidence Level and can be Used to Systematically Discard Frames Summary – ELITIST Approach

64. A Principled Method of Frame Selection (the ELITIST approach) can be Used to Improve the Accuracy of Articulatory Feature Classification The ELITIST Approach is Based on the Observation that Frames In the Center of Phonetic Segments are More Accurately Classified than Those at Segment Boundaries Frame Classification Accuracy is Highly Correlated with MLP Network Confidence Level and can be Used to Systematically Discard Frames Discarding such Low-Confidence Frames Improves AF Classification Summary – ELITIST Approach

65. A Principled Method of Frame Selection (the ELITIST approach) can be Used to Improve the Accuracy of Articulatory Feature Classification The ELITIST Approach is Based on the Observation that Frames In the Center of Phonetic Segments are More Accurately Classified than Those at Segment Boundaries Frame Classification Accuracy is Highly Correlated with MLP Network Confidence Level and can be Used to Systematically Discard Frames Discarding such Low-Confidence Frames Improves AF Classification Manner Classification is Sufficiently Improved as to be Capable of Performing Manner-Specific Training for Place-of-Articulation Features Summary – ELITIST Approach

66. A Principled Method of Frame Selection (the ELITIST approach) can be Used to Improve the Accuracy of Articulatory Feature Classification The ELITIST Approach is Based on the Observation that Frames In the Center of Phonetic Segments are More Accurately Classified than Those at Segment Boundaries Frame Classification Accuracy is Highly Correlated with MLP Network Confidence Level and can be Used to Systematically Discard Frames Discarding such Low-Confidence Frames Improves AF Classification Manner Classification is Sufficiently Improved as to be Capable of Performing Manner-Specific Training for Place-of-Articulation Features Place of Articulation Feature Classification Improves using Manner- Specific Training Summary – ELITIST Approach

67. A Principled Method of Frame Selection (the ELITIST approach) can be Used to Improve the Accuracy of Articulatory Feature Classification The ELITIST Approach is Based on the Observation that Frames In the Center of Phonetic Segments are More Accurately Classified than Those at Segment Boundaries Frame Classification Accuracy is Highly Correlated with MLP Network Confidence Level and can be Used to Systematically Discard Frames Discarding such Low-Confidence Frames Improves AF Classification Manner Classification is Sufficiently Improved as to be Capable of Performing Manner-Specific Training for Place-of-Articulation Features Place of Articulation Feature Classification Improves using Manner- Specific Training This Performance Enhancement is Probably the Result of: –Fewer features to classify for any given manner class –More homogeneous place-of-articulation training material Summary – ELITIST Approach

68. A Principled Method of Frame Selection (the ELITIST approach) can be Used to Improve the Accuracy of Articulatory Feature Classification The ELITIST Approach is Based on the Observation that Frames In the Center of Phonetic Segments are More Accurately Classified than Those at Segment Boundaries Frame Classification Accuracy is Highly Correlated with MLP Network Confidence Level and can be Used to Systematically Discard Frames Discarding such Low-Confidence Frames Improves AF Classification Manner Classification is Sufficiently Improved as to be Capable of Performing Manner-Specific Training for Place-of-Articulation Features Place of Articulation Feature Classification Improves using Manner- Specific Training This Performance Enhancement is Probably the Result of: –Fewer features to classify for any given manner class –More homogeneous place-of-articulation training material Such Improvements in AF Classification Accuracy Can Be Used to Improve the Quality of Automatic Phonetic Annotation Summary – ELITIST Approach

69. Part Three THE ELITIST APPROACH GOES DUTCH Description of the Development Corpus - VIOS The Nature of Cross-Linguistic Transfer of Articulatory Features Application of the ELITIST Approach to Dutch Manner-Specific, Place-of-Articulation Classification for Dutch

70. Extemporaneous, Prompted Human-Machine Telephone Dialogues –Human speakers querying an automatic system for Dutch Railway timetables –Wide range of dialect variability, both genders, variation in speaker age Dutch Development Corpus – VIOS

71. Extemporaneous, Prompted Human-Machine Telephone Dialogues –Human speakers querying an automatic system for Dutch Railway timetables –Wide range of dialect variability, both genders, variation in speaker age A Portion of the Corpus Manually Labeled at the Phonetic-Segment Level –Material labeled by speech science students at Nijmegen University –This component of the corpus served as the testing material –There was 18 minutes of material in this portion of the corpus Dutch Development Corpus – VIOS

72. Extemporaneous, Prompted Human-Machine Telephone Dialogues –Human speakers querying an automatic system for Dutch Railway timetables –Wide range of dialect variability, both genders, variation in speaker age A Portion of the Corpus Manually Labeled at the Phonetic-Segment Level –Material labeled by speech science students at Nijmegen University –This component of the corpus served as the testing material –There was 18 minutes of material in this portion of the corpus The Major Portion of the Corpus Automatically Labeled and Segmented –The automatic method incorporated a certain degree of pronunciation-model knowledge derived from language-specific phonological rules –This part of the corpus served as the training material –There was 60 minutes of material in this portion of the corpus Dutch Development Corpus – VIOS

73. How Dutch Differs from English Dutch and English are Genetically Closely Related Languages –Perhaps 1500 years of time depth separating the languages –They share some (but not all - see below) phonetic properties in common

74. How Dutch Differs from English Dutch and English are Genetically Closely Related Languages –Perhaps 1500 years of time depth separating the languages –They share some (but not all - see below) phonetic properties in common The “Dental” Place of Articulation is Present in English, but not in Dutch

75. How Dutch Differs from English Dutch and English are Genetically Closely Related Languages –Perhaps 1500 years of time depth separating the languages –They share some (but not all - see below) phonetic properties in common The “Dental” Place of Articulation is Present in English, but not in Dutch The Manner “Flap” is Present in English, but not in Dutch

76. How Dutch Differs from English Dutch and English are Genetically Closely Related Languages –Perhaps 1500 years of time depth separating the languages –They share some (but not all - see below) phonetic properties in common The “Dental” Place of Articulation is Present in English, but not in Dutch The Manner “Flap” is Present in English, but not in Dutch Certain Manner/Place Combinations in Dutch are not Found in English –For example – the velar fricative associated with orthographic “g”

77. How Dutch Differs from English Dutch and English are Genetically Closely Related Languages –Perhaps 1500 years of time depth separating the languages –They share some (but not all - see below) phonetic properties in common The “Dental” Place of Articulation is Present in English, but not in Dutch The Manner “Flap” is Present in English, but not in Dutch Certain Manner/Place Combinations in Dutch are not Found in English –For example – the velar fricative associated with orthographic “g” The Vocalic System (particularly diphthongs) Differs Between Dutch and English

78. Cross-Linguistic Classification Classification Accuracy on the VIOS Corpus –Results depend on whether the classifiers were trained on VIOS (Dutch) or NTIMIT (English) material

79. Cross-Linguistic Classification Classification Accuracy on the VIOS Corpus –Results depend on whether the classifiers were trained on VIOS (Dutch) or NTIMIT (English) material –Voicing and manner classification is comparable between the two training corpora

80. Cross-Linguistic Classification Classification Accuracy on the VIOS Corpus –Results depend on whether the classifiers were trained on VIOS (Dutch) or NTIMIT (English) material –Voicing and manner classification is comparable between the two training corpora –Place classification is significantly worse when training on NTIMIT

81. Cross-Linguistic Classification Classification Accuracy on the VIOS Corpus –Results depend on whether the classifiers were trained on VIOS (Dutch) or NTIMIT (English) material –Voicing and manner classification is comparable between the two training corpora –Place classification is significantly worse when training on NTIMIT –Other feature dimensions exhibit only slightly worse performance training on NTIMIT

82. For VIOS-trained Classifiers Frames with Confidence Levels Below “Threshold” are Discarded –Setting the threshold to 0.7 filters out ca. 15% of the frames, corresponding to 6% of the segments The Elitist Approach Applied to Dutch

83. For VIOS-trained Classifiers Frames with Confidence Levels Below “Threshold” are Discarded –Setting the threshold to 0.7 filters out ca. 15% of the frames, corresponding to 6% of the segments The Accuracy of MANNER Frame Classification Improves –Frame-level classification accuracy increases from 85% to 91% The Elitist Approach Applied to Dutch

84. For VIOS-trained Classifiers Frames with Confidence Levels Below “Threshold” are Discarded –Setting the threshold to 0.7 filters out ca. 15% of the frames, corresponding to 6% of the segments The Accuracy of MANNER Frame Classification Improves –Frame-level classification accuracy increases from 85% to 91% For NTIMIT-trained Classifiers (but classifying VIOS material) Frames with Confidence Levels Below “Threshold” are Discarded –Setting the threshold to 0.7 filters out ca. 19% of the frames The Elitist Approach Applied to Dutch

85. For VIOS-trained Classifiers Frames with Confidence Levels Below “Threshold” are Discarded –Setting the threshold to 0.7 filters out ca. 15% of the frames, corresponding to 6% of the segments The Accuracy of MANNER Frame Classification Improves –Frame-level classification accuracy increases from 85% to 91% For NTIMIT-trained Classifiers (but classifying VIOS material) Frames with Confidence Levels Below “Threshold” are Discarded –Setting the threshold to 0.7 filters out ca. 19% of the frames The Accuracy of MANNER Frame Classification Improves –Frame-level classification accuracy increases from 73% to 81% The Elitist Approach Applied to Dutch

86. Although There are Nine Distinct Place of Articulation Features Overall Place of Articulation is Manner-Dependent

87. Although There are Nine Distinct Place of Articulation Features Overall For Any Single Manner Class There are Only Three Place Features Place of Articulation is Manner-Dependent

88. Although There are Nine Distinct Place of Articulation Features Overall For Any Single Manner Class There are Only Three Place Features The Locus of Articulation Constriction Differs Among Manner Classes Place of Articulation is Manner-Dependent

89. Thus, if the Manner is Classified Correctly, this Information can be Exploited to Enhance Place of Articulation Classification Place of Articulation is Manner-Dependent

90. Thus, if the Manner is Classified Correctly, this Information can be Exploited to Enhance Place of Articulation Classification Thus, Each Manner Class can be Trained on Comparable Relational Place Features: ANTERIOR CENTRAL POSTERIOR Place of Articulation is Manner-Dependent

91. Thus, if the Manner is Classified Correctly, this Information can be Exploited to Enhance Place of Articulation Classification Thus, Each Manner Class can be Trained on Comparable Relational Place Features: ANTERIOR CENTRAL POSTERIOR Knowing the “Manner” Improves “Place” Classification for both Consonants and Vowels in DUTCH Place of Articulation is Manner-Dependent VIOS (telephone) Corpus

92. Knowing the “Manner” Improves “Place” Classification for the “Approximant” Segments in DUTCH VIOS (telephone) Corpus Manner-Specific Place Classification – Dutch

93. Knowing the “Manner” Improves “Place” Classification for the “Approximant” Segments in DUTCH Approximants are Classified as “Vocalic” Rather Than as “Consonantal” VIOS (telephone) Corpus Manner-Specific Place Classification – Dutch

94. Summary – ELITIST Goes Dutch Cross-linguistic Transfer of Articulatory Features – Classifiers are more than 80% correct on all AF dimensions except for “place” when trained and tested on VIOS

95. Summary – ELITIST Goes Dutch Cross-linguistic Transfer of Articulatory Features – Classifiers are more than 80% correct on all AF dimensions except for “place” when trained and tested on VIOS – Voicing and manner classification is comparable between VIOS and NTIMIT

96. Summary – ELITIST Goes Dutch Cross-linguistic Transfer of Articulatory Features – Classifiers are more than 80% correct on all AF dimensions except for “place” when trained and tested on VIOS – Voicing and manner classification is comparable between VIOS and NTIMIT – Place classification (for VIOS) is much worse when trained on NTIMIT

97. Summary – ELITIST Goes Dutch Cross-linguistic Transfer of Articulatory Features – Classifiers are more than 80% correct on all AF dimensions except for “place” when trained and tested on VIOS – Voicing and manner classification is comparable between VIOS and NTIMIT – Place classification (for VIOS) is much worse when trained on NTIMIT – Other AF dimensions are only slightly worse when trained on NTIMIT

98. Summary – ELITIST Goes Dutch Cross-linguistic Transfer of Articulatory Features – Classifiers are more than 80% correct on all AF dimensions except for “place” when trained and tested on VIOS – Voicing and manner classification is comparable between VIOS and NTIMIT – Place classification (for VIOS) is much worse when trained on NTIMIT – Other AF dimensions are only slightly worse when trained on NTIMIT Application of the ELITIST Approach to the VIOS Corpus – Results improve when the ELITIST approach is used

99. Summary – ELITIST Goes Dutch Cross-linguistic Transfer of Articulatory Features – Classifiers are more than 80% correct on all AF dimensions except for “place” when trained and tested on VIOS – Voicing and manner classification is comparable between VIOS and NTIMIT – Place classification (for VIOS) is much worse when trained on NTIMIT – Other AF dimensions are only slightly worse when trained on NTIMIT Application of the ELITIST Approach to the VIOS Corpus – Results improve when the ELITIST approach is used – Training on VIOS: frame-level classification accuracy increases from 85% to 91% (15% of the frames discarded)

100. Summary – ELITIST Goes Dutch Cross-linguistic Transfer of Articulatory Features – Classifiers are more than 80% correct on all AF dimensions except for “place” when trained and tested on VIOS – Voicing and manner classification is comparable between VIOS and NTIMIT – Place classification (for VIOS) is much worse when trained on NTIMIT – Other AF dimensions are only slightly worse when trained on NTIMIT Application of the ELITIST Approach to the VIOS Corpus – Results improve when the ELITIST approach is used – Training on VIOS: frame-level classification accuracy increases from 85% to 91% (15% of the frames discarded) – Training on NTIMIT: frame-level classification accuracy increases from 73% to 81% (19% of frames discarded)

101. Summary – ELITIST Goes Dutch Cross-linguistic Transfer of Articulatory Features – Classifiers are more than 80% correct on all AF dimensions except for “place” when trained and tested on VIOS – Voicing and manner classification is comparable between VIOS and NTIMIT – Place classification (for VIOS) is much worse when trained on NTIMIT – Other AF dimensions are only slightly worse when trained on NTIMIT Application of the ELITIST Approach to the VIOS Corpus – Results improve when the ELITIST approach is used – Training on VIOS: frame-level classification accuracy increases from 85% to 91% (15% of the frames discarded) – Training on NTIMIT: frame-level classification accuracy increases from 73% to 81% (19% of frames discarded) Manner-Specific Classification for Place of Articulation Features – Knowing the “manner” improves “place” classification for vowels and for consonants

102. Summary – ELITIST Goes Dutch Cross-linguistic Transfer of Articulatory Features – Classifiers are more than 80% correct on all AF dimensions except for “place” when trained and tested on VIOS – Voicing and manner classification is comparable between VIOS and NTIMIT – Place classification (for VIOS) is much worse when trained on NTIMIT – Other AF dimensions are only slightly worse when trained on NTIMIT Application of the ELITIST Approach to the VIOS Corpus – Results improve when the ELITIST approach is used – Training on VIOS: frame-level classification accuracy increases from 85% to 91% (15% of the frames discarded) – Training on NTIMIT: frame-level classification accuracy increases from 73% to 81% (19% of frames discarded) Manner-Specific Classification for Place of Articulation Features – Knowing the “manner” improves “place” classification for vowels and for consonants – Accuracy increases between 10 and 20% (absolute) for all “place” features

103. Summary – ELITIST Goes Dutch Cross-linguistic Transfer of Articulatory Features – Classifiers are more than 80% correct on all AF dimensions except for “place” when trained and tested on VIOS – Voicing and manner classification is comparable between VIOS and NTIMIT – Place classification (for VIOS) is much worse when trained on NTIMIT – Other AF dimensions are only slightly worse when trained on NTIMIT Application of the ELITIST Approach to the VIOS Corpus – Results improve when the ELITIST approach is used – Training on VIOS: frame-level classification accuracy increases from 85% to 91% (15% of the frames discarded) – Training on NTIMIT: frame-level classification accuracy increases from 73% to 81% (19% of frames discarded) Manner-Specific Classification for Place of Articulation Features – Knowing the “manner” improves “place” classification for vowels and for consonants – Accuracy increases between 10 and 20% (absolute) for all “place” features – Approximants are classified as “vocalic” not “consonantal” – knowing the “manner” improves “place” classification for “approximant” segments

104. Part Four INTO THE FUTURE Towards Fully Automatic Transcription Systems An Empirically Oriented Discipline Based on Annotated Corpora

105. The Eternal Pentangle Phonetic and Prosodic Annotation is Limited in Quantity

106. The Eternal Pentangle Phonetic and Prosodic Annotation is Limited in Quantity –This material is important for understanding spoken language and developing superior technology for recognition and synthesis

107. The Eternal Pentangle Phonetic and Prosodic Annotation is Limited in Quantity –This material is important for understanding spoken language and developing superior technology for recognition and synthesis

108. I Have a Dream, That One Day ….

109. There will be Annotated Corpora for All Major Languages of the World

110. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation)

111. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About:

112. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features

113. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments

114. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation

115. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units

116. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units –Lexical representations

117. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units –Lexical representations –Prosodic information pertaining to accent and intonation

118. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units –Lexical representations –Prosodic information pertaining to accent and intonation –Morphological patterns, as well as syntactic and grammatical material

119. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units –Lexical representations –Prosodic information pertaining to accent and intonation –Morphological patterns, as well as syntactic and grammatical material –Semantics and its relation to the lower tiers of spoken language

120. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units –Lexical representations –Prosodic information pertaining to accent and intonation –Morphological patterns, as well as syntactic and grammatical material –Semantics and its relation to the lower tiers of spoken language –Audio and video detail pertaining to all aspects of spoken language

121. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units –Lexical representations –Prosodic information pertaining to accent and intonation –Morphological patterns, as well as syntactic and grammatical material –Semantics and its relation to the lower tiers of spoken language –Audio and video detail pertaining to all aspects of spoken language That a Science of Spoken Language will be Empirically Based

122. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units –Lexical representations –Prosodic information pertaining to accent and intonation –Morphological patterns, as well as syntactic and grammatical material –Semantics and its relation to the lower tiers of spoken language –Audio and video detail pertaining to all aspects of spoken language That a Science of Spoken Language will be Empirically Based –Using these annotated corpora to perform detailed statistical analyses

123. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units –Lexical representations –Prosodic information pertaining to accent and intonation –Morphological patterns, as well as syntactic and grammatical material –Semantics and its relation to the lower tiers of spoken language –Audio and video detail pertaining to all aspects of spoken language That a Science of Spoken Language will be Empirically Based –Using these annotated corpora to perform detailed statistical analyses –Generating hypotheses about the organization and function of spoken language

124. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units –Lexical representations –Prosodic information pertaining to accent and intonation –Morphological patterns, as well as syntactic and grammatical material –Semantics and its relation to the lower tiers of spoken language –Audio and video detail pertaining to all aspects of spoken language That a Science of Spoken Language will be Empirically Based –Using these annotated corpora to perform detailed statistical analyses –Generating hypotheses about the organization and function of spoken language –Performing experiments based on insights garnered from such corpora

125. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units –Lexical representations –Prosodic information pertaining to accent and intonation –Morphological patterns, as well as syntactic and grammatical material –Semantics and its relation to the lower tiers of spoken language –Audio and video detail pertaining to all aspects of spoken language That a Science of Spoken Language will be Empirically Based –Using these annotated corpora to perform detailed statistical analyses –Generating hypotheses about the organization and function of spoken language –Performing experiments based on insights garnered from such corpora That Such Corpora will be Used to Develop Wonderful Technology

126. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units –Lexical representations –Prosodic information pertaining to accent and intonation –Morphological patterns, as well as syntactic and grammatical material –Semantics and its relation to the lower tiers of spoken language –Audio and video detail pertaining to all aspects of spoken language That a Science of Spoken Language will be Empirically Based –Using these annotated corpora to perform detailed statistical analyses –Generating hypotheses about the organization and function of spoken language –Performing experiments based on insights garnered from such corpora That Such Corpora will be Used to Develop Wonderful Technology –To create “flawless” speech recognition

127. I Have a Dream, That One Day …. There will be Annotated Corpora for All Major Languages of the World (generated by automatic means, but based on manual annotation) That Each of These Corpora will Contain Detailed Information About: –Articulatory-acoustic features –Phonetic segments –Pronunciation variation –Syllable units –Lexical representations –Prosodic information pertaining to accent and intonation –Morphological patterns, as well as syntactic and grammatical material –Semantics and its relation to the lower tiers of spoken language –Audio and video detail pertaining to all aspects of spoken language That a Science of Spoken Language will be Empirically Based –Using these annotated corpora to perform detailed statistical analyses –Generating hypotheses about the organization and function of spoken language –Performing experiments based on insights garnered from such corpora That Such Corpora will be Used to Develop Wonderful Technology –To create “flawless” speech recognition –And “perfect” speech synthesis

128. That’s All, Folks Many Thanks for Your Time and Attention