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.
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The Syllable in Japanese?
Labrune (2012): no positive psycholinguistic evidence for the “cognitive reality” of the syllable in Japanese.
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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’)
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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
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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
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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?
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Two Approaches
Corpus study Experiment (prelim. results)
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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.
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Four Variables
Coda-N Vi (ai, ui, ei, oi) Long Vowels Inter-voiceless-consonant i and u
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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
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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
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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”
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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
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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
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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
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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.
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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
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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
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