1. Predicting Phrasing and Accent
Julia Hirschberg
CS 6998
12/1/2018

2. A car bomb attack on a police station in the northern Iraqi city of Kirkuk early Monday killed four civilians and wounded 10 others U.S. military officials said. A leading Shiite member of Iraq's Governing Council on Sunday demanded no more "stalling" on arranging for elections to rule this country once the U.S.-led occupation ends June 30. Abdel Aziz al-Hakim a Shiite cleric and Governing Council member said the U.S.-run coalition should have begun planning for elections months ago.
12/1/2018

3. Why predict phrasing and accent?
TTS and CTS
Naturalness
Intelligibility
Recognition
Decrease perplexity
Modify durational predictions for words at phrase boundaries
Identify most ‘salient’ words
12/1/2018

4. How predict phrasing and accent?
Default prosodic assignment from simple text analysis
Hand-built rule-based system: hard to modify and adapt to new domains
Corpus-based approaches (Sproat et al ’92)
Train prosodic variation on large labeled corpora using machine learning techniques
Accent and phrasing decisions
TTS default prosodic assignment developed to be independent of domains and tasks. Uses simple text analysis to vary phrasing, accent, possibly pitch range.
While hand-built rule-sets are still used for particular application domains, most systems have moved toward automatically trained prosodic assignment systems.
12/1/2018

5. Associate prosodic labels with simple features of transcripts
distance from beginning or end of phrase
orthography: punctuation, paragraphing
part of speech, constituent information
Apply learned rules to new text
Note: problems in labeling consistency of e.g. phrase boundaries. How to produce a gold standard??
12/1/2018

6. Prosodic Phrasing 2 `levels’ of phrasing in ToBI
intermediate phrase: one or more pitch accents plus a phrase accent (H or L- )
intonational phrase: one or more intermediate phrases + boundary tone (H% or L% )
ToBI break-index tier
0 no word boundary
1 word boundary
2 strong juncture with no tonal markings
3 intermediate phrase boundary
4 intonational phrase boundary
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7. What are the indicators of phrasing in speech?
Timing
Pause
Lengthening
F0 changes
Vocal fry/glottalization
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8. What linguistic and contextual features are linked to phrasing?
Syntactic structure?
We only suspected they all knew that a burglary had been committed.
Abney ’91 chunking
Steedman ’90, Oehrle ’91 CCGs …
Length of sentence
Word co-occurrence statistics
I should have known and so should you have.
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9. Are words on each side accented or not?
Where is previous phrase boundary?
Part-of-speech of words around potential boundary site
Largest constituent dominating w(i) but not w(j)
Smallest constituent dominating w(i),w(j)
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10. Statistical learning methods
Classification and regression trees (CART)
Rule induction (Ripper)
HMMs
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11. Evaluation: Hard! How to define Gold Standard? Natural speech corpus
Multi-speaker/same text
Subjective judgments
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12. Today Incremental improvements continue:
Adding higher-accuracy parsing (Koehn et al ‘00)
Collins ‘99 parser
More sophisticated learning algorithms (Schapire & Singer ‘00)
Better representations: tree based?
Rules always impoverished
Where to next?
12/1/2018

13. Pitch Accent/Prominence in ToBI
Which items are made intonationally prominent and how?
Accent type:
H* simple high (declarative)
L* simple low (ynq)
L*+H scooped, late rise (uncertainty/ incredulity)
L+H* early rise to stress (contrastive focus)
H+!H* fall onto stress (implied familiarity)
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14. What are the indicators of accent?
F0 excursion
Durational lengthening
Voice quality
Vowel quality
Loudness
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15. What are the correlates of accent in text and context?
Word class: content vs. function words
Information status:
Given/new
Topic/Focus
Contrast
Grammatical function
The ball touches the cone.
Surface position: Today George is hungry.
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16. Association with focus: Dogs must be carried.
John only introduced Mary to Sue.
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17. How can we capture such information simply?
POS window
Position of candidate word in sentence
Location of prior phrase boundary
Pseudo-given/new
Location of word in complex nominal and stress prediction for that nominal
Word co-occurrence
12/1/2018

18. Today Concept-to-Speech (CTS) – Pan&McKeown99
systems should be able to specify “better” prosody: the system knows what it wants to say and can specify how
New features!
New learning methods:
Boosting and bagging (Sun02)
Combine text and acoustic cues for ASR
Co-training??
In principle, the information TTS systems lack to support natural prosodic assignment is readily available to CTS systems. So the initial hope in the NLG community was that prosodic assignment would be a simple problem. It’s proven however fairly hard. Why?
12/1/2018

19. MAGIC MM system for presenting cardiac patient data
Developed at Columbia by McKeown and colleagues in conjunction with Columbia Presbyterian Medical Center to automate post-operative status reporting for bypass patients
Uses mostly traditional NLG hand-developed components
Generate text, then annotate prosodically
Corpus-trained prosodic assignment component
To give an example of how more traditional NLG systems are coping with these problems, I want to briefly describe some work Shimei Pan has done for her doctoral thesis at Columbia University with Kathy McKeown and me on training a prosody module for a medical domain.
12/1/2018

20. Corpus: written and oral patient reports
50min multi-speaker, spontaneous + 11min single speaker, read
1.24M word text corpus of discharge summaries
Transcribed, ToBI labeled
Generator features labeled/extracted:
syntactic function
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21. semantic ‘informativeness’ (rarity in corpus)
p.o.s.
semantic category
semantic ‘informativeness’ (rarity in corpus)
semantic constituent boundary location and length
salience
given/new
focus
theme/ rheme
‘importance’
‘unexpectedness’
Lessons I personally learned from this:
First, just because CTS systems have more information doesn’t mean this information will be useful for prosodic assignment.
Second, there is something unsatisfactory about viewing prosodic decisions being made after the text has been constructed.
12/1/2018

22. Very hard to label features
Results: new features to specify TTS prosody
Of CTS-specific features only semantic informativeness (likeliness of occurring in a corpus) useful so far
Looking at context, word collocation for accent placement helps predict accent
RED CELL (less predictable) vs. BLOOD cell (more)
Most predictable words are accented less frequently (40-46%) and least predictable more (73-80%)
Unigram+bigram model predicts accent status w/77% (+/-.51) accuracy
12/1/2018

23. Future Intonation Prediction
Assigning affect (emotion)
Personalizing TTS
Conveying personality, charisma?
12/1/2018

24. Next Week
Look at 2 other phenomena we’d like to capture in TTS systems:
Information status
Discourse (topic) structure
Homework 2 is due on Monday!
12/1/2018