An Elitist Approach to Articulatory-Acoustic Feature

An Elitist Approachto

Articulatory-Acoustic Feature Classification in English and in Dutch

Steven Greenberg, Shawn Chang and Mirjam WesterInternational Computer Science Institute1947 Center Street, Berkeley, CA 94704

http://www.icsi.berkeley.edu/[email protected]

http://www.icsi.berkeley.edu/[email protected]

Mirjam WesterA2 RT, Department of Language and Speech

Nijmegen University, Netherlandshttp://www.lands.let.kun.nl/Tspublic/wester

[email protected]

Acknowledgements and Thanks

Automatic Feature Classification and AnalysisJoy Hollenback, Lokendra Shastri, Rosaria Silipo

Research FundingU.S. National Science FoundationU.S. Department of Defense

Motivation for Automatic Transcription• Many Properties of Spontaneous Spoken Language Differ from Those of

Laboratory and Citation Speech


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 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






• Manual Annotation of Phonetic and Prosodic Material is a Pain in the Butt to Produce– Hand labeling and segmentation is time consuming and expensive






• 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








– Forced-alignment-based segmentation is poor - ca. 40% off on phone boundaries– Phone classification error is ca. 30-50%








– 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









• Automatic Transcription is Likely to Aid in the Development of Speech Recognition and Synthesis Technology

– And therefore is worth the effort to develop

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



• 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



• 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

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

• 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


• Provides Training Material for Technology Applications– Automatic speech recognition, particularly pronunciation models– Speech synthesis, ditto– Cross-linguistic transfer of technology algorithms



• 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-Centric View of Spoken LanguageOur Focus in Today’s Presentation is on Articulatory Feature Classification

Other levels of linguistic representation are also extremely important to annotate

Our Focus

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



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




• 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


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




• AFs are Potentially More Effective in Speech Recognition Systems– More accurate and flexible pronunciation models (tied to syllabic and lexical units)






• 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






• 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 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)

• 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



• 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)






• 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)


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

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)

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

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)

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

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

• 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

• 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

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


• MLP Network Confidence Level is Highly Correlated with Frame Accuracy

• The Most Confidently Classified Frames are Generally More Accurate

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

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

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

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

• The Accuracy of MANNER Frame Classification Improves– Frame-level classification accuracy increases overall from 85% to 93%

The Elitist Approach to Manner Classification

• 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

• 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

• 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

Manner-Specific Place ClassificationThus, Each Manner Class can be Trained on Comparable Relational Place

Features:ANTERIOR CENTRAL POSTERIOR

Manner-Specific Place Classification

NTIMIT (telephone) Corpus

Thus, Each Manner Class can be Trained on Comparable Relational Place Features:

ANTERIOR CENTRAL POSTERIORClassifying 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

Manner-Specific Classification – Vowels• Knowing the “Manner” Improves “Place” Classification for Vowels as Well• Also Improves “Height” Classification


Manner-Specific Place Classification - Overall


• Overall, Performance Improves Between 5% and 14% (in absolute terms)• Improvement is Greatest for Stops, Nasals and Flaps

• A Principled Method of Frame Selection (the ELITIST approach) can be Used to Improve the Accuracy of Articulatory Feature

Classification

Summary – ELITIST Approach


Classification• The ELITIST Approach is Based on the Observation that Frames In the

Center of Phonetic Segments are More Accurately Classified thanThose at Segment Boundaries





• Frame Classification Accuracy is Highly Correlated with MLP Network Confidence Level and can be Used to Systematically Discard

Frames






Frames• Discarding such Low-Confidence Frames Improves AF Classification






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








• 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


• 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


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

• 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


• 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



• 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


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”





• 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

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



– Voicing and manner classification is comparable between the two training corpora– Place classification is significantly worse when training on NTIMIT


– 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

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



• 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





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



• Although There are Nine Distinct Place of Articulation Features Overall

Place of Articulation is Manner-Dependent


• For Any Single Manner Class There are Only Three Place Features



• For Any Single Manner Class There are Only Three Place Features

• The Locus of Articulation Constriction Differs Among Manner Classes


• 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



• 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


VIOS (telephone) Corpus

• Knowing the “Manner” Improves “Place” Classification for the “Approximant” Segments in DUTCH


Manner-Specific Place Classification – Dutch

• Knowing the “Manner” Improves “Place” Classification for the “Approximant” Segments in DUTCH• Approximants are Classified as “Vocalic” Rather Than as “Consonantal”


Manner-Specific Place Classification – Dutch

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


– 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


– 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


– 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



• 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






• 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






• 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

Part Four

INTO THE FUTURE

Towards Fully Automatic Transcription Systems

An Empirically Oriented Discipline Based on Annotated Corpora

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


– This material is important for understanding spoken language and developing superior technology for recognition and synthesis

I Have a Dream, That One Day ….

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)


(generated by automatic means, but based on manual annotation)• That Each of These Corpora will Contain Detailed Information About:



– Articulatory-acoustic features



– Articulatory-acoustic features– Phonetic segments



– Articulatory-acoustic features– Phonetic segments– Pronunciation variation



– Articulatory-acoustic features– Phonetic segments– Pronunciation variation– Syllable units



– Articulatory-acoustic features– Phonetic segments– Pronunciation variation– Syllable units– Lexical representations



– Articulatory-acoustic features– Phonetic segments– Pronunciation variation– Syllable units– Lexical representations– Prosodic information pertaining to accent and intonation



– 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



– 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



– 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




• That a Science of Spoken Language will be Empirically Based– Using these annotated corpora to perform detailed statistical analyses




• 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




• 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





• That Such Corpora will be Used to Develop Wonderful Technology– To create “flawless” speech recognition





• That Such Corpora will be Used to Develop Wonderful Technology– To create “flawless” speech recognition– And “perfect” speech synthesis

That’s All, FolksMany Thanks for Your Time and Attention