2014-7-labphon

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Un- der the sea
su- ba- ra- shii
The syllable as a prosodic unit in
Japanese lexical strata: evidence
from text-setting
Rebecca L Starr
National University of Singapore
Stephanie S Shih
University of California, Merced
LabPhon 14
National Institute for Japanese Linguistics
25 – 27 July 2014
1
The Mora in Japanese

Japanese = prototypical example of a
“mora-based” language
e.g., kai.zen = [ka]μ[i]μ.[ze]μ[n]μ
o
Rhythmic timing depends on mora units
(Homma 1981; Port et al. 1987; cf., Beckman 1982)
o
o
Phonological/morphological implications:
e.g., accent placement, compensatory
lengthening
Traditional Japanese poetry = mora counting
e.g., haiku 5 – 7 – 5 mora form
2
The Syllable in Japanese?


Syllable usually treated as unnecessary or
unimportant in Japanese phonology (Labrune
2012).
Kubozono (1999; et seq.): Japanese is (in part) a
syllable-based language.
o
accent placement, word formation, etc.
3
The Syllable in Japanese?

Labrune (2012): no positive psycholinguistic
evidence for the “cognitive reality” of the
syllable in Japanese.
4
Lexical Strata in Japanese

Major lexical strata in Japanese (Itô and Mester
1999):

Yamato (native Japanese)


Sino-Japanese (Chinese origin)
•

Ex:人間 ningen (‘human’)
Foreign (~85% English origin)
•

Ex: 好きsuki (‘to like’)
Ex: ベンチ benchi (‘bench’)
Mimetic

Ex: フワフワ fuwafuwa (‘fluffy’)
5
Lexical Strata in Japanese

Strata characterized by different phonotactics,
and phonological rules (Itô and Mester 1999).
o


e.g., long a never occurs in Sino-Japanese words.
Because Chinese and English are syllable-based,
is it possible that the syllable is a more salient
unit in the Sino and Foreign strata?
Alternatively, does widespread knowledge of
English contribute to increased salience of the
syllable in just the Foreign stratum?
6
Evidence from Text-Setting


Text-setting: the pairing of language and
music in song.
Typologically, text-setting makes use of salient
prosodic units particular to a language.
o
English: syllables, lexical and phrasal stresses
(Halle & Lerdhal 1993; Shih 2008; Hayes 2009; a.o.)
o

Cantonese: tonal melodies matched in musical
melodies (Yung 1991)
Similar claims for metrical typology (Hanson and
Kiparksy 1996)
7
Evidence from Text-Setting

Japanese text-setting:

Claimed to operate as a mora-based system
(Kubozono 1999; Hayes and Swiger 2008; cf. Manabe 2009)
= each mora must receive (at least) one note.
e.g., do.ra.go-n bo-o.ru (Dragonball Z theme, 1989)
x x x x x x x = 7 notes
8
Evidence from Text-Setting

BUT, multi-moraic notes show up frequently
in modern Japanese songs.


Similar findings in poetry (Tanaka 2012)
In these settings, it is the syllable that receives
at least one note.

e.g., do- ra- gon boo-ru
x x x x x = 5 notes
9
Main Research Questions


What constraints govern moraic vs. nonmoraic text-setting variation in Japanese?
Do Japanese listeners perceive the syllable as
an acceptable segmentation unit in textsetting?
o
Is it as acceptable as the mora?
10
Two Approaches


Corpus study
Experiment (prelim. results)
12
Corpus Study
Three corpora of Japanese songs compared:
Anime theme songs
native
Late 1980s – 90s
Disney songs
translated
Late 1980s – 90s
Christmas songs
translated
Late 19th c. – 21st c.
13
Four Variables




Coda-N
Vi (ai, ui, ei, oi)
Long Vowels
Inter-voiceless-consonant i and u
14
Example settings
Example
ningen (‘human’)
sekai (‘world’)
hoshii (‘want’)
suki (‘like’)
Moraic
ni-n-ge-n
se-ka-i
ho-shi-i
sɯ-ki, sɯ̥-ki
Syllabic
nin-gen
se-kai
ho-shii
s(ɯ̥ )ki
15
Example Clip

“Santa” appears in moraic and syllabic
settings within the same song:
sa n ta no o ji sa n ga
de mo so no san ta wa
(I Saw Mommy Kissing Santa Claus, 1952, trans. 1962)
16
Results: Difference by lexical
stratum
100%
90%
80%
70%
60%
50%
non-m
moraic
40%
30%
20%
10%
0%
foreign non-foreign
Coda-N
foreign non-foreign
foreign non-foreign
Vi
Long Vowels
foreign non-foreign
Inter-voiceless
i and u
Syllabic
Moraic
17
Results: Differences by corpus
18
Results: Regression modeling
o
o
o
Generalized linear mixed-effect model (glmer).
Significantly more syllabic settings in translated
song corpora than native corpus.
Significantly more syllabic settings in Foreign
stratum words.
o
o
No reliable difference between Sino and Yamato
strata words.
Other phonological factors also significant for
certain variables: e.g., sonority scale for
diphthongs (e.g., ai vs. ei; Prince 1983)
19
Experiment
• Corpus study demonstrates that trained
composers use both moraic and syllabic
settings and prefer to use syllabic settings for
Foreign stratum words.
• Do ordinary Japanese speakers follow the
same patterns?
– What about Japanese learners?
• Perceptual experiment to test acceptability of
different text-setting styles.
21
Novel methodology: Vocaloid
• Creating stimuli involving multiple sung
arrangements presents a challenge.
• We used Yamaha’s Vocaloid 3 software, which
produces synthesized sung Japanese.
“sakura”
22
Factors
• Linguistic variables:
– Coda-N
– Vi (limited to ai)
• Strata: Foreign vs. Sino
– Not possible to make minimal pairs with Yamato
– Selected near-minimal pairs.
• Ex: ベンチbenchi / 便宜 bengi
23
Factors
• Settings tested:
– Mora:
mi-n-to da-yo
– Syllable:
min-to da-yo-ne
– Split Syllable(melisma):
mi-in-to da-yo
– Bad 1 (too small):
m-in-to da-yo
– Bad 2 (too large):
mi-i-i-i-intodayo
24
Methodology
• Motsu-kun is learning to arrange lyrics to a
melody. Help him improve by rating his work!
ミントだよね
(minto dayone)
• Asked to rate on 1-4
Likert scale.
26
Participants & Implementation
• 18 native Japanese speakers
– Asked for frequency of English use
• 10 Japanese learners (Eng & Chi native spkrs)
– Asked for length of Japanese study
• Survey conducted online using Qualtrics.
– Still recruiting participants: http://bit.ly/1oblYNz
• Data analyzed using lmer (linear mixed-model
regression) in R.
27
Findings
28
Key Findings
• Native speakers rated Syllabic (min-to) just as
highly as Moraic setting (mi-n-to).
• Split-Syll. (mi-in-to) was rated significantly lower
than Syll., but significantly higher than Bad1 or
Bad2.
 Conclusions:
o Native listeners prefer a one-to-one correspondence
between note and prosodic unit, whether mora or
syllable.
o When there is room in the melody for a moraic
setting, syllable-based is dispreferred, but not totally
rejected.
29
Key Findings
• Japanese learners rate Moraic, Syll., and Split-Syll.
settings as equally good.
– Moraic settings, which do not occur in English and
Chinese, are just as highly rated as familiar syllablebased settings.
– Learners must be acquiring familiarity with moraic
segmentation as part of the Japanese learning
process.
– But learners do not pick up that Split-Syll (mi-in-to) is
dispreferred.
• Not as sensitive to vowel length?
• Don’t care about one-to-one match between notes and
prosodic unit?
30
Key Findings
• Trend, but no significant differences between
Foreign and Sino strata (contrary to corpus
findings).
• No effects of English exposure or whether
currently living in Japan.
• More participants needed.
31
Conclusions: Experiment
• Positive evidence for the cognitive reality of
the syllable in Japanese:
– Japanese listeners fully accept syllable-based
segmentation in contexts where moraic
segmentation is impractical.
– When moraic segmentation is available, syllablebased segmentation is dispreferred, but rated
more highly than settings which segment along
non-salient prosodic boundaries.
32
Overall Conclusions
• Both syllabic and moraic text-setting styles are
prevalent and acceptable in Japanese.
• Text-setting style is conditioned by factors
such as phonological context and (possibly)
lexical stratum.
• Native listeners prefer a one-to-one
correspondence between notes and prosodic
units, whether mora or syllable.
33
Thank you!
Acknowledgements to Noriko Manabe, Reiko Kataoka, Roey Gafter,
Junko Ito, Mie Hiramoto, Yosuke Sato, Sakiko Kajino, Nala Lee, and
Jason Ginsburg for their input and assistance.
どうも ありがとう
ございました.
contact:
[email protected]
[email protected]
34
Select references
Beckman, Mary. 1982. Segment Duration and the 'Mora' in Japanese. Phonetica. 39. 113-135.
Halle, John and Fred Lerdahl. 1993. A Generative Text-setting Model. Current Musicology. 55: 3-21.
Hanson, Kristin and Paul Kiparsky. 1996. A Parametric Theory of Poetic Meter. Language. 72(2). 287-335.
Hayes, Bruce. 2009. Textsetting as Constraint Conflict. In Jean-Louis Aroui and Andy Arleo (ed). Towards a
Typology of Poetic Forms: From language to metrics and beyond. Amsterdam: John Benjamins. 43-62.
Hayes, Bruce and Tami Swiger. 2008. Two Japanese Children’s Songs. MS. University of California, Los
Angeles. 12 pages.
Homma, Yayoi. 1981. Durational relationship between Japanese stops and vowels. Journal of Phonetics 9
(3): 273 – 281.
Itô, Junko & Armin Mester. 1999. Japanese Phonology. In John Goldsmith (ed). The Handbook of
Phonological Theory. Oxford: Wiley-Blackwell. 818-838.
Kubozono, Haruo. 1999. Mora and Syllable. In Natsuko Tsujimura (ed). The Handbook of Japanese
Linguistics. Oxford: Wiley-Blackwell. 31-61.
Labrune, Laurence. 2012. Questioning the universality of the syllable: evidence from Japanese. Phonology
29:1, 113 - 152.
Manabe, Noriko. 2009.Western Music in Japan: The evolution of styles in children’s songs, hip-hop, and
other genres. Ph.D. dissertation, CUNY Graduate Center.
Pellegrino, Francois, Coupe, Christophe, and Egidio Marsico. 2011. A Cross-Language Perspective on
Speech Information Rate. Language. 87(3). 539 – 558.
Port, Robert. F., Dalby, Jonathan & O'Dell, Michael. (1987). Evidence for mora-timing in Japanese. Journal of
the Acoustical Society of America. 81. 1574 – 1585.
Prince, Alan. 1983. Relating to the Grid. Linguistic Inquiry. 11:511-562.
Shih, Stephanie. 2008. Text-Setting: a (musical) analogy to poetic meter. Paper presented at New Research
Programs in the Linguistics of Literature. University of California, Berkeley.
Tanaka, Shin’ichi. 2012. Syllable Neutralization and Prosodic Unit in Japanese Senryu Poems. Paper
presented at Metrics, Music and Mind. Rome, Italy.
Yung, Bell. 1991. The Relationship of Text and Tune in Chinese Opera. In J. Sundberg; L. Nord; and R.
35
Carlson (ed). Music, Language, Speech, and Brain. London: Macmillan. 408-418.
Anime Corpus

11 songs total:
o
o
o
o
o
o
o
o
o
o
o
Totoro (1988)
Dragonball Z (1989)
Kiki’s Delivery Service (1989)
All-Purpose Cultural Cat-Girl Nuku Nuku (1990)
Gundam F91 (1991)
Bubblegum Crash (1991)
Slayers (1995)
Sailor Moon (1995)
Tenchi Universe (1995)
Slayers Next (1996)
Boys Before Flowers (1996)
36
Disney Corpus

Films:
o
o
o
o
o

The Little Mermaid (1989 translation)
Beauty and the Beast (1991)
Aladdin (1992)
The Lion King (1994)
The Little Mermaid (1997 translation)
17 songs total
37
Christmas Corpus

10 songs total:










Hark the Herald Angels Sing (1888)
O Holy Night (1909)
Silent Night (1909)
Jingle Bells (1958)
Santa Claus is Coming to Town (1959)
Rudolph the Red-Nosed Reindeer (1959)
I Saw Mommy Kissing Santa Claus (1962)
Winter Wonderland (1962)
The Christmas Song (1996)
We Wish You a Merry Christmas (2005)
38
Coding Methodology

Excluded:
o
Code-switching involving two or more consecutive
words in English with different parts of speech.
o
o
o
o
o
Ex: “It’s my day”
Content that was spoken rather than sung.
Discourse particles like ああ aa.
Nonsense words for which a stratum could not be
determined.
Mimetic stratum words (not enough for analysis).
39
Experiment: statistical models

Native speakers LMER
REML criterion at convergence: 422.5
Scaled residuals:
Min
1Q
Median 3Q
Max
-2.12822 -0.70806 -0.07082 0.59577 2.71389
Random effects:
Groups Name
Variance
Std.Dev.
participant (Intercept) 0.2245
0.4739
Residual
0.4958
0.7041
Number of obs: 180, groups: participant, 18
Fixed effects:
Estimate Std. Error df
(Intercept) 1.861e+00 1.703e-01 5.663e+01
settings2 5.556e-01 1.660e-01 1.570e+02
settingb1 9.444e-01 1.660e-01 1.570e+02
settingb2 1.972e+00 1.660e-01 1.570e+02
settingm -2.778e-01 1.660e-01 1.570e+02
t value
10.929
3.347
5.690
11.883
-1.674
Pr(>|t|)
1.33e-15 ***
0.00102 **
6.05e-08 ***
< 2e-16 ***
0.09619
40
Experiment: statistical models

Learners LMER
REML criterion at convergence: 240.9
Scaled residuals:
Min
1Q
Median 3Q
Max
-2.3779 -0.7192 0.0632 0.5096 2.3131
Random effects:
Groups Name
Variance
Std.Dev.
participant (Intercept)
0.1830
0.4278
Residual
0.5378
0.7334
Number of obs: 100, groups: participant, 10
Fixed effects:
Estimate Std. Error
(Intercept) 1.7555 0.2375
settingb1 0.7500 0.2319
settingb2 1.7238 0.2339
settingm -0.1369 0.2324
settings2 0.2131 0.2324
stratsino 0.1200 0.1467
df
42.1900
83.9900
84.0200
84.0000
84.0000
83.9900
t value
7.391
3.234
7.369
-0.589
0.917
0.818
Pr(>|t|)
3.92e-09 ***
0.00175 **
1.09e-10 ***
0.55744
0.36181
0.41560
41
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