1. Context and Learning in Multilingual Tone and Pitch Accent Recognition Gina-Anne Levow University of Chicago May 18, 2007

2. Roadmap Challenges for Tone and Pitch Accent –Contextual effects –Training demands Modeling Context for Tone and Pitch Accent –Data collections & processing –Integrating context –Context in Recognition Asides: More tones and features Reducing Training Demands –Data collections & structure –Semi-supervised learning –Unsupervised clustering Conclusion

3. Challenges: Context Tone and Pitch Accent Recognition –Key component of language understanding Lexical tone carries word meaning Pitch accent carries semantic, pragmatic, discourse meaning –Non-canonical form (Shen 90, Shih 00, Xu 01) Tonal coarticulation modifies surface realization –In extreme cases, fall becomes rise –Tone is relative To speaker range –High for male may be low for female To phrase range, other tones –E.g. downstep

4. Challenges: Training Demands Tone and pitch accent recognition –Exploit data intensive machine learning SVMs (Thubthong 01,Levow 05, SLX05) Boosted and Bagged Decision trees (X. Sun, 02) HMMs: (Wang & Seneff 00, Zhou et al 04, Hasegawa-Johnson et al, 04,… –Can achieve good results with huge sample sets SLX05: ~10K lab syllabic samples -> > 90% accuracy –Training data expensive to acquire Time – pitch accent 10s of times real-time Money – requires skilled labelers Limits investigation across domains, styles, etc –Human language acquisition doesn’t use labels

5. Strategy: Overall Common model across languages –Common machine learning classifiers –Acoustic-prosodic model No word label, POS, lexical stress info No explicit tone label sequence model –English, Mandarin Chinese, isiZulu (also Cantonese)

6. Strategy: Context Exploit contextual information –Features from adjacent syllables Height, shape: direct, relative –Compensate for phrase contour –Analyze impact of Context position, context encoding, context type > 12.5% reduction in error over no context

7. Data Collections: I English: (Ostendorf et al, 95) –Boston University Radio News Corpus, f2b –Manually ToBI annotated, aligned, syllabified –Pitch accent aligned to syllables Unaccented, High, Downstepped High, Low –(Sun 02, Ross & Ostendorf 95)

8. Data Collections: II Mandarin: –TDT2 Voice of America Mandarin Broadcast News –Automatically force aligned to anchor scripts Automatically segmented, pinyin pronunciation lexicon Manually constructed pinyin-ARPABET mapping CU Sonic – language porting –High, Mid-rising, Low, High falling, Neutral

9. Data Collections: III isiZulu: (Govender et al., 2005) –Sentence text collected from Web Selected based on grapheme bigram variation –Read by male native speaker –Manually aligned, syllabified –Tone labels assigned by 2 nd native speaker Based only on utterance text –Tone labels: High, low

10. Local Feature Extraction Uniform representation for tone, pitch accent –Motivated by Pitch Target Approximation Model Tone/pitch accent target exponentially approached –Linear target: height, slope (Xu et al, 99) Base features: –Pitch, Intensity max, mean, min, range (Praat, speaker normalized) –Pitch at 5 points across voiced region –Duration –Initial, final in phrase Slope: –Linear fit to last half of pitch contour

11. Context Features Local context: –Extended features Pitch max, mean, adjacent points of preceding, following syllables –Difference features Difference between –Pitch max, mean, mid, slope –Intensity max, mean Of preceding, following and current syllable Phrasal context: –Compute collection average phrase slope –Compute scalar pitch values, adjusted for slope

12. Classification Experiments Classifier: Support Vector Machine –Linear kernel –Multiclass formulation SVMlight (Joachims), LibSVM (Cheng & Lin 01) –4:1 training / test splits Experiments: Effects of –Context position: preceding, following, none, both –Context encoding: Extended/Difference –Context type: local, phrasal

13. Results: Local Context ContextMandarin ToneEnglish Pitch Accent isiZulu Tone Full74.5%81.3%75.9% Extend PrePost74%80.7%73.8% Extend Pre74%79.9%73.6% Extend Post70.5%76.7%72.3% Diffs PrePost75.5%80.7%75.8% Diffs Pre76.5%79.5%75.5% Diffs Post69%77.3%72.8% Both Pre76.5%79.7%75.5% Both Post71.5%77.6%72.5% No context68.5%75.9%72.2%

14. Results: Local Context ContextMandarin ToneEnglish Pitch Accent isiZulu Tone Full74.5%81.3%75.9% Extend PrePost74%80.7%73.8% Extend Pre74%79.9%73.6% Extend Post70.5%76.7%72.3% Diffs PrePost75.5%80.7%75.8% Diffs Pre76.5%79.5%75.5% Diffs Post69%77.3%72.8% Both Pre76.5%79.7%75.5% Both Post71.5%77.6%72.5% No context68.5%75.9%72.2%

15. Results: Local Context ContextMandarin ToneEnglish Pitch Accent isiZulu Tone Full74.5%81.3%75.9% Extend PrePost74%80.7%73.8% Extend Pre74%79.9%73.6% Extend Post70.5%76.7%72.3% Diffs PrePost75.5%80.7%75.8% Diffs Pre76.5%79.5%75.5% Diffs Post69%77.3%72.8% Both Pre76.5%79.7%75.5% Both Post71.5%77.6%72.5% No context68.5%75.9%72.2%

16. Results: Local Context ContextMandarin ToneEnglish Pitch Accent Full74.5%81.3% Extend PrePost74%80.7% Extend Pre74%79.9% Extend Post70.5%76.7% Diffs PrePost75.5%80.7% Diffs Pre76.5%79.5% Diffs Post69%77.3% Both Pre76.5%79.7% Both Post71.5%77.6% No context68.5%75.9%

17. Discussion: Local Context Any context information improves over none –Preceding context information consistently improves over none or following context information English/isiZulu: Generally more context features are better Mandarin: Following context can degrade –Little difference in encoding (Extend vs Diffs) Consistent with phonetic analysis (Xu) that carryover coarticulation is greater than anticipatory

18. Results & Discussion: Phrasal Context Phrase ContextMandarin ToneEnglish Pitch Accent Phrase75.5%81.3% No Phrase72%79.9% Phrase contour compensation enhances recognition Simple strategy Use of non-linear slope compensate may improve

19. Context: Summary Employ common acoustic representation –Tone (Mandarin), pitch accent (English) Cantonese: ~64%; 68% with RBF kernel SVM classifiers - linear kernel: 76%, 81% Local context effects: –Up to > 20% relative reduction in error –Preceding context greatest contribution Carryover vs anticipatory Phrasal context effects: –Compensation for phrasal contour improves recognition

20. Context: Summary Employ common acoustic representation –Tone (Mandarin,isiZulu), pitch accent (English) SVM classifiers - linear kernel: 76%,76%, 81% Local context effects: –Up to > 20% relative reduction in error –Preceding context greatest contribution Carryover vs anticipatory Phrasal context effects: –Compensation for phrasal contour improves recognition

21. Aside: More Tones Cantonese: –CUSENT corpus of read broadcast news text –Same feature extraction & representation –6 tones: –High level, high rise, mid level, low fall, low rise, low level –SVM classification: Linear kernel: 64%, Gaussian kernel: 68% –3,6: 50% - mutually indistinguishable (50% pairwise) »Human levels: no context: 50%; context: 68% Augment with syllable phone sequence –86% accuracy: 90% of syllable w/tone 3 or 6: one dominates

22. Aside: Voice Quality & Energy w/ Dinoj Surendran Assess local voice quality and energy features for tone –Not typically associated with tones: Mandarin/isiZulu Considered: –VQ: NAQ, AQ, etc; Spectral balance; Spectral Tilt; Band energy Useful: Band energy significantly improves –Mandarin: neutral tone Supports identification of unstressed syllables –Spectral balance predicts stress in Dutch –isiZulu: Using band energy outperforms pitch In conjunction with pitch -> ~78%

23. Roadmap Challenges for Tone and Pitch Accent –Contextual effects –Training demands Modeling Context for Tone and Pitch Accent –Data collections & processing –Integrating context –Context in Recognition Reducing Training Demands –Data collections & structure –Semi-supervised learning –Unsupervised clustering Conclusion

24. Strategy: Training Challenge: –Can we use the underlying acoustic structure of the language – through unlabeled examples – to reduce the need for expensive labeled training data? Exploit semi-supervised and unsupervised learning –Semi-supervised Laplacian SVM –K-means and asymmetric k-lines clustering –Substantially outperform baselines Can approach supervised levels

25. Data Collections & Processing English: (as before) –Boston University Radio News Corpus, f2b Binary: Unaccented vs accented 4-way: Unaccented, High, Downstepped High, Low Mandarin: –Lab speech data: (Xu, 1999) 5 syllable utterances: vary tone, focus position –In-focus, pre-focus, post-focus –TDT2 Voice of America Mandarin Broadcast News –4-way: High, Mid-rising, Low, High falling isiZulu: (as before) –Read web sentences 2-way: High vs low

26. Semi-supervised Learning Approach: –Employ small amount of labeled data –Exploit information from additional – presumably more available –unlabeled data Few prior examples: several weakly supervised: (Wong et al, ’05) Classifier: –Laplacian SVM (Sindhwani,Belkin&Niyogi ’05) –Semi-supervised variant of SVM Exploits unlabeled examples –RBF kernel, typically 6 nearest neighbors, transductive

27. Experiments Pitch accent recognition: –Binary classification: Unaccented/Accented –1000 instances, proportionally sampled Labeled training: 200 unacc, 100 acc –80% accuracy (cf. 84% w/15x labeled SVM) Mandarin tone recognition: –4-way classification: n(n-1)/2 binary classifiers –400 instances: balanced; 160 labeled Clean lab speech- in-focus-94% – cf. 99% w/SVM, 1000s train; 85% w/SVM 160 training samples Broadcast news: 70% –Cf. < 50% w/SVM 160 training samples

28. Unsupervised Learning Question: –Can we identify the tone structure of a language from the acoustic space without training? Analogous to language acquisition Significant recent research in unsupervised clustering Established approaches: k-means Spectral clustering (Shi & Malik ‘97, Fischer & Poland 2004): asymmetric k-lines –Little research for tone Self-organizing maps (Gauthier et al,2005) –Tones identified in lab speech using f0 velocities Cluster-based bootstrapping (Narayanan et al, 2006) Prominence clustering (Tambourini ’05)

29. Clustering Pitch accent clustering: –4 way distinction: 1000 samples, proportional 2-16 clusters constructed –Assign most frequent class label to each cluster Classifier: –Asymmetric k-lines: »context-dependent kernel radii, non-spherical –> 78% accuracy: 2 clusters: asymmetric k-lines best –Context effects: Vector w/preceding context vs vector with no context comparable

30. Contrasting Clustering Contrasts: –Clustering: 3 Spectral approaches: –Perform spectral decomposition of affinity matrix »Asymmetric k-lines (Fischer & Poland 2004) »Symmetric k-lines (Fischer & Poland 2004) »Laplacian Eigenmaps (Belkin, Niyogi, & Sindhwani 2004) » Binary weights, k-lines clustering K-means: Standard Euclidean distance –# of clusters: 2-16 Best results: > 78% –2 clusters: asymmetric k-lines; > 2 clusters: kmeans Larger # clusters: all similar

31. Contrasting Learners

32. Tone Clustering: I Mandarin four tones: 400 samples: balanced 2-phase clustering: 2-5 clusters each Asymmetric k-lines, k-means clustering –Clean read speech: In-focus syllables: 87% (cf. 99% supervised) In-focus and pre-focus: 77% (cf. 93% supervised) –Broadcast news: 57% (cf. 74% supervised) –K-means requires more clusters to reach k-lines level

33. Tone Structure First phase of clustering splits high/rising from low/falling by slope Second phase by pitch height

34. Tone Clustering: II isiZulu High/Low tones 3225 samples: no labels Proportional: ~62% low, 38% high K-means clustering: 2 clusters –Read speech, web-based sentences 70% accuracy (vs 76% fully-supervised)

35. Conclusions Common prosodic framework for tone and pitch accent recognition –Contextual modeling enhances recognition Local context and broad phrase contour –Carryover coarticulation has larger effect for Mandarin –Exploiting unlabeled examples for recognition Semi- and Un-supervised approaches –Best cases approach supervised levels with less training –Exploits acoustic structure of tone and accent space

36. Current and Future Work Interactions of tone and intonation –Recognition of topic and turn boundaries –Effects of topic and turn cues on tone real’n Child-directed speech & tone learning Support for Computer-assisted tone learning Structured sequence models for tone –Sub-syllable segmentation & modeling Feature assessment –Band energy and intensity in tone recognition

37. Thanks Dinoj Surendran, Siwei Wang, Yi Xu Natasha Govender and Etienne Barnard V. Sindhwani, M. Belkin, & P. Niyogi; I. Fischer & J. Poland; T. Joachims; C-C. Cheng & C. Lin This work supported by NSF Grant #0414919 http://people.cs.uchicago.edu/~levow/tai