1. Meaningful Intonational Variation
11/12/2018

2. Today Assigning variation for TTS, CTS Contours Pitch Range
Accent
Phrasing
Pitch Range
Amplitude and timing
11/12/2018

3. TTS Production Pipeline
Orthographic input: Dr. Smith lives on Elm Dr.
Text normalization: abbreviation expansion…
Pronunciation modeling: POS id, WS disambiguation
Intonation assignment: parsing, POS id, robust semantics…
Phonetic/phonological realization: phonological parsing, phonetic analysis
Unit selection: acoustic analysis
11/12/2018

4. Intonation Assignment: Phrasing
Traditional: hand-built rules
Punctuation
Context/function word: no breaks after function word He went to dinner
Parse? She favors the nuts and bolts approach
Current: statistical analysis of large labeled corpus
Punctuation, pos window, utt length,…
11/12/2018

5. Functions of Phrasing
Disambiguates syntactic constructions, e.g. PP attachment:
S: You should buy the ticket with the discount coupon.
Disambiguates scope ambiguities, e.g. Negation:
S: You aren’t booked through Rome because of the fare.
Or modifier scope:
S: This fare is restricted to retired politicians and civil servants.
11/12/2018

6. Intonation Assignment: Accent
Hand-built rules
Function/content distinction He went out the back door/He threw out the trash
Complex nominals:
Main Street/Park Avenue
city hall parking lot
Statistical procedures trained on large corpora
Contrastive stress, given/new distinction?
11/12/2018

7. Functions of Pitch Accent
Given/new information
S: Do you need a return ticket.
U: No, thanks, I don’t need a return.
Contrast (narrow focus)
U: No, thanks, I don’t need a RETURN…. (I need a time schedule, receipt,…)
Disambiguation of discourse markers
S: Now let me get you the train information.
U: Okay (thanks) vs. Okay….(but I really want…)
11/12/2018

8. Intonation Assignment: Contours
Simple rules
‘.’ = declarative contour
‘?’ = yes-no-question contour unless wh-word present at/near front of sentence
Well, how did he do it? And what do you know?
What else might we do?
11/12/2018

9. Contours: Accent + Phrasing
What do intonational contours ‘mean’ (Ladd ‘80, Bolinger ‘89)?
Speech acts (statements, questions, requests)
S: That’ll be credit card? (L* H- H%)
Propositional attitude (uncertainty, incredulity)
S: You’d like an evening flight. (L*+H L- H%)
Speaker affect (anger, happiness, love)
U: I said four SEVEN one! (L+H* L- L%)
“Personality”
S: Welcome to the Sunshine Travel System.
11/12/2018

10. Propositional attitude (uncertainty) Did you feed the animals?
I fed the L*+H goldfish L-H%
Distinguish direct/indirect speech acts
Can you open the door?
11/12/2018

11. The TTS Front End Today
Corpus-based statistical methods instead of hand-built rule-sets
Dictionaries instead of rules (but fall-back to rules)
Modest attempts to infer contrast, given/new
Text analysis tools: pos tagger, morphological analyzer, little parsing
11/12/2018

12. Where good match between input and database
TTS: Where are we now?
Natural sounding speech for some utterances
Where good match between input and database
Still…hard to vary prosodic features and retain naturalness
Yes-no questions: Do you want to fly first class?
Context-dependent variation still hard to infer from text and hard to realize naturally:
11/12/2018

13. Appropriate contours from text
Emphasis, de-emphasis to convey focus, given/new distinction: I own a cat. Or, rather, my cat owns me.
Variation in pitch range, rate, pausal duration to convey topic structure
Characteristics of ‘emotional speech’ little understood, so hard to convey: …a voice that sounds friendly, sympathetic, authoritative….
How to mimic real voices?
11/12/2018

14. TTS vs. CTS
Decisions in Text-to-Speech (TTS) depend on syntax, information status, topic structure,… information explicitly available to NLG
Concept-to-Speech (CTS) systems should be able to specify “better” prosody: the system knows what it wants to say and can specify how
But….generating prosody for CTS isn’t so easy
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?
11/12/2018

15. To(nes and)B(reak)I(ndices)
Developed by prosody researchers in four meetings over
Goals:
devise common labeling scheme for Standard American English that is robust and reliable
promote collection of large, prosodically labeled, shareable corpora
ToBI standards also proposed for Japanese, German, Italian, Spanish, British and Australian English,....
11/12/2018

16. Minimal ToBI transcription: recording of speech f0 contour ToBI tiers:
orthographic tier: words
break-index tier: degrees of junction (Price et al ‘89)
tonal tier: pitch accents, phrase accents, boundary tones (Pierrehumbert ‘80)
miscellaneous tier: disfluencies, non-speech sounds, etc.
11/12/2018

17. Sample ToBI Labeling
11/12/2018

18. Online training material,available at:
Evaluation
Good inter-labeler reliability for expert and naive labelers: 88% agreement on presence/absence of tonal category, 81% agreement on category label, 91% agreement on break indices to within 1 level (Silverman et al. ‘92,Pitrelli et al ‘94)
11/12/2018

19. 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)
11/12/2018

20. Downstepped accents: !H*, L+!H*, L*+!H Degree of prominence:
within a phrase: HiF0
across phrases
11/12/2018

21. Prosodic Phrasing in ToBI
‘Levels’ of phrasing:
intermediate phrase: one or more pitch accents plus a phrase accent (H or L- )
intonational phrase: 1 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
11/12/2018

22. L*+H L* H*
H-H%
H-L%
L-H%
L-L%
11/12/2018

23. H* !H*
H+!H*
L+H*
H-H%
H-L%
L-H%
L-L%
11/12/2018

24. Contour Examples
11/12/2018

25. And Other Things Contribute: Pitch Range and Timing (Rate, Pause)
Level of speaker engagement
Hello vs. HELLO
Contour interpretation
Rise/fall/rise (L*+H L-H%): Elephantiasis isn’t incurable
Discourse/topic structure: paratones
11/12/2018

26. Corpus-Based Research
Predicting accent, phrasing, contours from large ToBI-labeled corpora
Features:
Word position, p.o.s. window, word cooccurence, punctuation, capitalization, sentence length, paragraph position, …
Results:
~80-85% correct accent prediction
~92-96% correct phrase boundary prediction
Contours????
Reality…
11/12/2018

27. I don’t want cereal I want toast. ….
This is my version of a rather long sentence which ideally should be broken into several phrases automatically by a smart system but we don't know if this will actually happen do we?
Is a yes-no question uttered with falling intonation? Does that sound delightful? Mellifluous?
I don’t want cereal I want toast. ….
11/12/2018

28. Next:
Story analysis and generation (readings will be available later this week – we’ll send mail)
11/12/2018