Managing Ambiguity, Gaps, and Errors in Spoken Language Processing Gina-Anne Levow May 14, 2009.

Managing Ambiguity, Gaps, and Errors in Spoken Language Processing

Gina-Anne LevowMay 14, 2009

Research Background & Interests

Dialogue Spoken and Multimodal Dialogue Systems

Recognizing spoken corrections (Levow 98,99,01,04) Predicting turn-taking behavior (Levow 05)

Recognizing prominence and tone (Levow 05,06,08,WL07,DL08)

Topic and story segmentation (Levow 04+, Matveeva&Levow 07)

Prosodic and lexical evidence Search and retrieval (Meng et al 01, Levow 03,07,Levow et al 05)

Focus on speech, cross-language News, conversation/interview data

Roadmap Motivation Tone and Pitch Accent

Challenges: Contextual variation & Training demands Data collections and processing Modeling Context for Tone and Pitch Accent

Aside: Novel features Reducing Training Demands: Semi- and Un-supervised

Learning Cross-language Spoken Document Retrieval

Challenges: Ambiguity, Gaps, and Errors Retrieval setting Multi-scale processing:

Phrases to subwords: translation, indexing, and expansion Conclusion

Challenges Growing opportunities for speech applications

Explosive growth in audio data Ubiquitous mobile devices

Systems rely on supporting resources and technologies

May be limited Labeled training data for task, linguistic resources

Or noisy Speech recognition, machine translation

Successful systems must remain effective Employ techniques to mitigate the impact of

ambiguity, gaps, and errors

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

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

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 (also isiZulu, Cantonese)

Strategy: Context Exploit contextual information

Features from adjacent syllables Height, shape: direct, relative

Compensate for phrase contour

Analyze impact of Context position, context encoding Up to 24% reduction in error over no context

English Data Collection Boston University Radio News Corpus

(Ostendorf et al, 95)

Manually ToBI annotated, aligned, syllabified

Pitch accent aligned to syllables Unaccented, High, Downstepped High, Low

(Sun 02, Ross & Ostendorf 95) Unaccented vs Accented

Mandarin Data Collections 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

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)

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

Context Features Local context:

Extended features 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

Supervised Classification 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

Results: Local ContextContext Mandarin Tone English

Pitch Accent

Full 74.5% 81.3%

Extend PrePost

74% 80.7%

Extend Pre 74% 79.9%

Extend Post 70.5% 76.7%

Diffs PrePost 75.5% 80.7%

Diffs Pre 76.5% 79.5%

Diffs Post 69% 77.3%

Both Pre 76.5% 79.7%

Both Post 71.5% 77.6%

No context 68.5% 75.9%

Context: Summary Employ common acoustic representation

Tone (Mandarin), pitch accent (English) SVM classifiers - linear kernel: 76%, 81% Local context effects:

Up to > 20% relative reduction in error Preceding context greatest contribution

Carryover vs anticipatory Consistent with phonetic analysis (Xu) that carryover

coarticulation is greater than anticipatory

Aside: Voice Quality & Energy

w/ Dinoj Surendran

Assess local voice quality and energy features for tone Not typically associated with tones: Mandarin

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



Aside: Novel features Reducing Training Demands: Semi- and Un-

supervised Learning Cross-language Spoken Document Retrieval



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 spectral clustering Substantially outperform baselines

Can approach supervised levels

Semi-supervised Learning Approach:

Employ small amount of labeled data Exploit information from additional –

presumably more available –unlabeled data Few prior exampes: self-training: Ostendorf TR

Classifier: Laplacian SVM (Sindhwani,Belkin&Niyogi ’05) Semi-supervised variant of SVM

Exploits unlabeled examples RBF kernel, typically 6 nearest neighbors, transductive

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

Pitch Accent Learning Curves

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)

Pitch Accent Clustering Clustering alternatives:

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

Assign most frequent class label to each cluster 4 way distinction: 1000 samples, proportional

Best results: > 78% 2 clusters: asymmetric k-lines; > 2 clusters: kmeans

Larger # clusters: all similar

Contrasting Learners

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

Tone Structure

First phase of clustering splits high/rising from low/falling by slopeSecond phase by pitch height

Discussion 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



Aside: Novel features Reducing Training Demands: Semi- and Un-supervised

Learning Cross-language Spoken Document Retrieval



Cross-language Spoken Document Retrieval

Explosive growth in online audio Increasing proportion non-English, esp. Chinese

Challenges: Ambiguity: many translations, acoustic confusion Gaps: OOV in recognition, translation (esp. NE) Errors: Misrecognition, mistranslation, misseg.

Solution I: subword units may help Transliteration, subword recognition, matching

Solution II: expansion may help Clean side collections provide terms

The Answer: A Preview Perform word-based speech recognition

Lexicon constraints greatly improve accuracy Perform phrase-based query translation

Minimizes translation ambiguity Convert both to character bigrams and match

Elegantly handles ambiguous term granularity Add evidence from proper name matching

Using syllable bigrams Perform document expansion to enhance

match

CL-SDR Architecture

DocumentExpansion

MandarinRetrievalSystem

MandarinTranscription

ComparableMandarin

Documents

BalancedGloss

Translation With

Stemming Backoff,χ2 Feature Selection

Inquery 3.1p1

Ranked list

VOAMandarinBroadcast

Audio:2265 stories

NYTEnglish

NewswireExemplars

Speech Recognition

Exhaustive RelevanceJudgments

Evaluation

17Topics

mAP

(Beginning from and building on the MEI project, Meng et al 01)

CL-SDR Architecture

DocumentExpansion

MandarinRetrievalSystem


ComparableMandarin

Documents

BalancedGloss

Translation With

Stemming Backoff,χ2 Feature Selection

Inquery 3.1p1

Ranked list


Audio:2265 stories

NYTEnglish

NewswireExemplars

Speech Recognition


Evaluation

17Topics

mAPMulti-scale points

Document Expansion

ComparableDocuments

RetrievalSystem

RankedDocument

ListTerm

Selection

ExpandedDocuments

Original Documents

DocumentsRetrievalSystem

(Singhal ’99)

Translation Granularity Ambiguity:

[[Human]7 [Rights] 30] 1

Phrases beat words

Three sources Translation lexicon Named entities Numeric expressions

Mean A

vera

ge P

reci

sion

Multi-scale Indexing and Expansion

Goal: Allow partial match,

minimize ambiguity Word segmentation:

Noisy, errorful in Mandarin text, speech

Single characters: ambiguous Overlapping char. bigrams:

Partial match, semantic units

Retrieval best w/character bigrams

Also outperform syllables Words improve expansion term

choice

Mean A

vera

ge P

reci

sion

Transliteration: Subword Translation

Untranslatable names Create cross-language

phonetic map Train transformation-based

error-driven learning on name translations

Produce 1-best syllables

Combine with standard term translation

Small, consistent improvement

Mean A

vera

ge P

reci

sion

Pre- and Post-translation Document Expansion

DocumentExpansion

EnglishRetrievalSystem


ComparableMandarin

Documents

BalancedGloss

Translation

Inquery 3.1p1

Ranked list


Audio:2265 stories

NYTEnglish

NewswireExemplars

Speech Recognition


Evaluation

17Topics

mAP

DocumentExpansion

ComparableEnglish

Documents

Bridging Gaps with Document Expansion

Prior work on query expansion (European)

Pre-translation most important – need terms

Pre-translation: Compensate for ASR errors,

more translatable Post-translation:

Recover trans./ASR gaps, errors, enrichment

Post improves significantly Outperforms mono, manual Recovers (trans.) OOV

Example terms: Tariq Aziz, Boris Yeltsin, etc

Less problematic in other lang.

Mean A

vera

ge P

reci

sion

Discussion Multi-scale processing enables:

Matching at highest level of precision to reduce ambiguity

Partial matching to enable robustness to gaps and errors

In conjunction with document expansion, Can outperform retrieval on monolingual,

manually transcriptions

Future Challenges SDR:

Good effectiveness shown for clean BN speech Conversational speech: more challenging

Higher WER will emphasize partial matching Systematic, statistically valid integration of:

ASR word/subword hypotheses at multiple scales Alternate retrieval models

CL-SDR: Integrate approaches that better model uncertainty

and ambiguity in translation

Future Challenges & Opportunities

Prominence and Tone Identify prominence and emphasis to improve

spoken language understanding Integrate with speech recognition

Rerank candidates Enhance unit selection for contextually

appropriate prosody Dialogue and intonation

Predicting and managing turn-taking Utterance classification Handling miscommunication

Thanks Dinoj Surendran, Siwei Wang, Yi Xu 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

MEI Team: Helen Meng, Sanjeev Khudanpur, Hsin-min Wang, Wai-Kit Lo, Doug Oard, et al.

Results & Discussion: Phrasal Context

Phrase Context

Mandarin Tone

English Pitch Accent

Phrase 75.5% 81.3%

No Phrase 72% 79.9%

•Phrase contour compensation enhances recognition•Simple strategy•Use of non-linear slope compensate may improve

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

Results: Local ContextContext Mandarin

ToneEnglish Pitch Accent

IsiZulu Tone

Full 74.5% 81.3% 76.2%

Extend PrePost

74% 80.7% 74.7%

Extend Pre 74% 79.9% 75.3%

Extend Post

70.5% 76.7% 74.6%

Diffs PrePost

75.5% 80.7% 76.2%

Diffs Pre 76.5% 79.5% 76.5%

Diffs Post 69% 77.3% 74.6%

Both Pre 76.5% 79.7% 76.5%

Both Post 71.5% 77.6% 74.8%

No context 68.5% 75.9% 74.1%



IsiZulu Tone

Full 74.5% 81.3% 76.2%

Extend PrePost

74% 80.7% 74.7%

Extend Pre 74% 79.9% 75.3%

Extend Post

70.5% 76.7% 74.6%

Diffs PrePost

75.5% 80.7% 76.2%

Diffs Pre 76.5% 79.5% 76.5%

Diffs Post 69% 77.3% 74.6%

Both Pre 76.5% 79.7% 76.5%

Both Post 71.5% 77.6% 74.8%

No context

68.5% 75.9% 74.1%



IsiZulu Tone

Full 74.5% 81.3% 76.2%

Extend PrePost

74% 80.7% 74.7%

Extend Pre

74% 79.9% 75.3%

Extend Post

70.5% 76.7% 74.6%

Diffs PrePost

75.5% 80.7% 76.2%

Diffs Pre 76.5% 79.5% 76.5%

Diffs Post 69% 77.3% 74.6%

Both Pre 76.5% 79.7% 76.5%

Both Post 71.5% 77.6% 74.8%

No context 68.5% 75.9% 74.1%

Confusion Matrix (English)

Recognized Tone

Manually Labeled Tone

Unaccented

High Low D.S. High

Unaccented 95%

25% 100%

53.5%

High 4.6%

73% 0% 38.5%

Low 0% 0% 0% 0%

D.S. High 0.3% 2% 0% 8%

Confusion Matrix (Mandarin)

Recognized Tone

Manually Labeled Tone

High Mid-Rising

Low High-Falling | Neutral

High 84% 9%

5%

13% | 0% |

Mid-Rising 6.7%

78.6%

10%

7% | 27.3% |

Low 0% 3.6% 70% 7% | 27.3%

High-Falling

7.4% 3.6% 10%

70% | 0% |

Neutral 0% 5.3% 5% 1.5% | 45%

Related Work Tonal coarticulation:

Xu & Sun,02; Xu 97;Shih & Kochanski 00 English pitch accent

X. Sun, 02; Hasegawa-Johnson et al, 04; Ross & Ostendorf 95

Lexical tone recognition SVM recognition of Thai tone: Thubthong

01 Context-dependent tone models

Wang & Seneff 00, Zhou et al 04

Pitch Target Approximation Model

Pitch target: Linear model:

Exponentially approximated:

In practice, assume target well-approximated by mid-point (Sun, 02)

battT )(

battty )exp()(

“Bounds” on Subword-Based Systems

Character bigrams for indexing

marginally outperforms word-based systems

Syllable bigrams are quite competitive,

though somewhat behind

Mean average precision ~0.6 is a good CL-SDR target

Cross-Language SDR Challenges

Query processing (translation) Tokenization Translation lexicon coverage Selection among alternate translations

Document processing (recognition) Tokenization Recognition lexicon coverage Selection among alternate recognition

hypotheses

Laplacian SVM For SVMs, we solve

Assume if samples close in intrinsic geometry, then conditionals P(y|x1) P(y|x2) are similar.

Add new regularizer, to control complexity in intrinsic geometry, as well as ambient.

For Laplacian SVM, solve Support of Px on compact submanifold

Manifold Regularization

Spectral clustering Employs spectrum of similarity

matrix S to perform dimensionality reduction for clustering Meila-Shi clusters based on eigenvectors

correspondings to k largest eigenvalues Laplacian eigenmaps:

Create edges for k nearest neighbors Choose weights: binary or heat kernel Compute eigenv’s for Ly = lambda Dy Represent by e-vectors of m smallest e-

values Preserves distances for near neighbors

K-lines

Given vectors y (spectral) Randomly generate vectors m

(or set to eigenvectors of y) Create matrices of all vectors y

closest to each mi mj = first eigenv of MjMjT Iterate

Asymmetric clustering

Clusterable data not just Gaussian New affinity measure: conductivity Question: Kernel radius?

Often just pick best Here, select automatically, context

dependent Also, asymmetric, e.g. if dense region, other

further Symmetric – take minimum

K-means clustering

Select k= number of clusters Randomly choose k points as cluster

centers A) Assign points to nearest cluster B) Recompute cluster centers Repeat A,B until convergence + Fast, large data sets - Random initial assignments

Sequence and Factorial Models: I

Sequence and Factorial Models

Compare 0-order, 1-order

Compare linear-chain vs factorial

Query byExample

EnglishNewswireExemplars

MandarinAudioStories

President Bill Clinton and Chinese President Jiang Zemin engaged in a spirited, televised debate Saturday over human rights and theTiananmen Square crackdown, and announced a string of agreements on arms control, energy and environmental matters. There were no announced breakthroughs on American human rights concerns, including Tibet, but both leaders accentuated the positive …

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Managing Ambiguity, Gaps, and Errors in Spoken Language Processing Gina-Anne Levow May 14, 2009.

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