Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-22T17:58:28.614Z Has data issue: false hasContentIssue false
  • English
  • Français

Explaining sonority projection effects*

Published online by Cambridge University Press:  21 July 2011

Robert Daland ,
Bruce Hayes ,
James White ,
Marc Garellek ,
Andrea Davis  and
Ingrid Norrmann
Robert Daland
Affiliation:
University of California, Los Angeles
Bruce Hayes
Affiliation:
University of California, Los Angeles
James White
Affiliation:
University of California, Los Angeles
Marc Garellek
Affiliation:
University of California, Los Angeles
Andrea Davis
Affiliation:
University of Arizona
Ingrid Norrmann
Affiliation:
University of California, Los Angeles
Get access Rights & Permissions [Opens in a new window]

Abstract

The term sonority projection refers to behavioural distinctions speakers make between unattested phonological sequences on the basis of sonority. For example, among onset clusters, the well-formedness relation [bn]>[lb] is observed in speech perception, speech production and non-word acceptability (Davidson 2006, 2007, Berent et al.2007, Albright, ms). We begin by replicating the sonority projection effects in a non-word acceptability study. Then we evaluate the extent to which sonority projection is predicted by existing computational models of phonotactics (Coleman & Pierrehumbert 1997, Hayes & Wilson 2008, inter alia). We show that a model based only on lexical statistics can explain sonority projection in English without a pre-existing sonority sequencing principle. To do this, a model must possess (i) a featural system supporting sonority-based generalisations, and (ii) a context representation including syllabification or equivalent information.

Type
Articles
Information
Phonology , Volume 28 , Issue 2 , August 2011 , pp. 197 - 234
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Albright, Adam (2009). Feature-based generalisation as a source of gradient acceptability. Phonology 26. 9–41.CrossRefGoogle Scholar
Albright, Adam (ms). Natural classes are not enough: biased generalization in novel onset clusters. MIT.Google Scholar
Baayen, R. Harald, Davidson, Douglas J. & Bates, Douglas M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language 59. 390–412.CrossRefGoogle Scholar
Baayen, R. Harald, Piepenbrock, Richard & Rijn, Hedderik van (1995). The CELEX lexical database. [CD-ROM: Release 2.] Philadelphia: Linguistic Data Consortium, University of Pennsylvania.Google Scholar
Baker, C. L. (1979). Syntactic theory and the projection problem. LI 10. 533–581.Google Scholar
Bailey, Todd M. & Hahn, Ulrike (2001). Determinants of wordlikeness: phonotactics or lexical neighborhoods? Journal of Memory and Language 44. 568–591.CrossRefGoogle Scholar
Baroni, Marco (2001). How do languages get crazy constraints? Phonetically-based phonology and the evolution of the Galeata Romagnolo vowel system. UCLA Working Papers in Linguistics 7: Papers in Phonology 5. 152–178.Google Scholar
Bates, David & Maechler, Martin (2009). Package ‘lme4’ (Version 0.999375-32): linear mixed-effects models using S4 classes. Available (April 2011) at http://cran.r-project.org/web/packages/lme4/lme4.pdf.Google Scholar
Berent, Iris (2008). Are phonological representations of printed and spoken language isomorphic? Evidence from the restrictions on unattested onsets. Journal of Experimental Psychology: Human Perception and Performance 34. 1288–1304.Google ScholarPubMed
Berent, Iris, Lennertz, Tracy, Jun, Jongho, Moreno, Miguel A. & Smolensky, Paul (2008). Language universals in human brains. Proceedings of the National Academy of Sciences 105. 5321–5325.CrossRefGoogle ScholarPubMed
Berent, Iris, Steriade, Donca, Lennertz, Tracy & Vaknin, Vered (2007). What we know about what we have never heard: evidence from perceptual illusions. Cognition 104. 591–630.CrossRefGoogle ScholarPubMed
Blevins, Juliette (2004). Evolutionary Phonology: the emergence of sound patterns. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Chomsky, Noam & Halle, Morris (1968). The sound pattern of English. New York: Harper & Row.Google Scholar
Clements, G. N. (1992). The Sonority Cycle and syllable organization. In Dressler, Wolfgang U., Luschützky, Hans C., Pfeiffer, Oskar E. & Rennison, John R. (eds.) Phonologica 1988. Cambridge: Cambridge University Press. 63–76.Google Scholar
Coady, Jeffry A. & Evans, Julia L. (2008). Uses and interpretations of non-word repetition tasks in children with and without specific language impairments (SLI). International Journal of Language Communication Disorders 43. 1–40.CrossRefGoogle ScholarPubMed
Coetzee, Andries W. (2008). Grammaticality and ungrammaticality in phonology. Lg 84. 218–257.Google Scholar
Coetzee, Andries W. (2009). Grammar is both categorical and gradient. In Parker, Stephen G. (ed.) Phonological argumentation: essays on evidence and motivation. London: Equinox. 9–42.Google Scholar
Coleman, John & Pierrehumbert, Janet B. (1997). Stochastic phonological grammars and acceptability. In Coleman, John (ed.) Proceedings of the 3rd Meeting of the ACL Special Interest Group in Computational Phonology. Somerset, NJ: Association for Computational Linguistics. 49–56.Google Scholar
Coppieters, René (1987). Competence differences between native and near-native speakers. Lg 63. 544–573.Google Scholar
Daland, Robert & Pierrehumbert, Janet B. (2011). Learning diphone-based segmentation. Cognitive Science 35. 119–155.CrossRefGoogle ScholarPubMed
Davidson, Lisa (2006). Phonology, phonetics, or frequency: influences on the production of non-native sequences. JPh 34. 104–137.Google Scholar
Davidson, Lisa (2007). The relationship between the perception of non-native phonotactics and loanword adaptation. Phonology 24. 261–286.CrossRefGoogle Scholar
Davidson, Lisa (2010). Phonetic bases of similarities in cross-language production: evidence from English and Catalan. JPh 38. 272–288.Google Scholar
Della Pietra, Stephen, Pietra, Vincent Della & Lafferty, John (1997). Inducing features of random fields. IEEE Transactions: Pattern Analysis and Machine Intelligence 19. 380–393.Google Scholar
Duanmu, San (2000). The phonology of Standard Chinese. Oxford: Oxford University Press.Google Scholar
Dupoux, Emmanuel, Kakehi, Kazuhiko, Hirose, Yuki, Pallier, Christophe & Mehler, Jacques (1999). Epenthetic vowels in Japanese: a perceptual illusion? Journal of Experimental Psychology: Human Perception and Performance 25. 1568–1578.Google Scholar
Edwards, Jan, Beckman, Mary E. & Munson, Benjamin (2004). The interaction between vocabulary size and phonotactic probability effects on children's production accuracy and fluency in nonword repetition. Journal of Speech, Language, and Hearing Research 47. 421–436.CrossRefGoogle ScholarPubMed
Frisch, Stefan A., Pierrehumbert, Janet B. & Broe, Michael B. (2004). Similarity avoidance and the OCP. NLLT 22. 179–228.Google Scholar
Frisch, Stefan A. & Zawaydeh, Bushra Adnan (2001). The psychological reality of OCP-place in Arabic. Lg 77. 91–106.Google Scholar
Gale, William A. & Sampson, Geoffrey (1995). Good-Turing frequency estimation without tears. Journal of Quantitative Linguistics 2. 217–237.CrossRefGoogle Scholar
Goldrick, Matthew (in press). Utilizing psychological realism to advance phonological theory. In Goldsmith, John A., Riggle, Jason & Yu, Alan (eds.) The handbook of phonological theory. 2nd edn. Oxford: Blackwell.Google Scholar
Goldsmith, John & Riggle, Jason (to appear). Information theoretic approaches to phonological structure: the case of vowel harmony. NLLT.Google Scholar
Goldwater, Sharon & Johnson, Mark (2003). Learning OT constraint rankings using a Maximum Entropy model. In Spenador, Jennifer, Eriksson, Anders & Dahl, Östen (eds.) Proceedings of the Stockholm Workshop on Variation within Optimality Theory. Stockholm: Stockholm University. 111–120.Google Scholar
Hay, Jennifer, Pierrehumbert, Janet B. & Beckman, Mary E. (2003). Speech perception, well-formedness and the statistics of the lexicon. In Local, John, Ogden, Richard & Temple, Rosalind (eds.) Phonetic interpretation: papers in laboratory phonology VI. Cambridge: Cambridge University Press. 58–74.Google Scholar
Hayes, Bruce (in press). Interpreting sonority-projection experiments: the role of phonotactic modeling. Proceedings of the 17th International Congress of Phonetic Sciences.Google Scholar
Hayes, Bruce, Kirchner, Robert & Steriade, Donca (eds.) (2004). Phonetically based phonology. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hayes, Bruce & Steriade, Donca (2004). Introduction: the phonetic bases of phonological Markedness. In Hayes, et al. . (2004). 1–33.Google Scholar
Hayes, Bruce & Wilson, Colin (2008). A maximum entropy model of phonotactics and phonotactic learning. LI 39. 379–440.Google Scholar
Hockett, Charles F. (1947). Peiping phonology. Journal of the American Oriental Society 67. 253–267. Reprinted in Martin Joos (ed.) (1966). Readings in linguistics I. 4th edn. Chicago: University of Chicago Press. 217–228.CrossRefGoogle Scholar
Hooper, Joan B. (1976). An introduction to natural generative phonology. New York: Academic Press.Google Scholar
Jespersen, Otto (1904). Lehrbuch der Phonetik. Leipzig & Berlin: Teubner.Google Scholar
Joseph, John E. (1995). Natural grammar, arbitrary lexicon: an enduring parallel in the history of linguistic thought. Language and Communication 15. 213–225.CrossRefGoogle Scholar
Jurafsky, Daniel & Martin, James H. (2009). Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. 2nd edn. New Jersey: Prentice Hall.Google Scholar
Kager, René & Pater, Joe (ms). Phonotactics as phonology: knowledge of a complex restriction in Dutch. University of Utrecht & University of Massachusetts, Amherst.Google Scholar
Kawahara, Shigeto (2008). Phonetic naturalness and unnaturalness in Japanese loanword phonology. Journal of East Asian Linguistics 17. 317–330.CrossRefGoogle Scholar
Kawahara, Shigeto (2011). Modes of phonological judgment. Ms, Rutgers University. Available as ROA-1140 from the Rutgers Optimality Archive.Google Scholar
Koo, Hahn & Cole, Jennifer (2006). On learnability and naturalness as constraints on phonological grammar. In Botinis, Antonis (ed.) Proceedings of ISCA Tutorial and Research Workshop on Experimental Linguistics. Athens: University of Athens. 165–168.Google Scholar
Lee, Yongsung (1994). Onset analysis of Korean on-glides. In Kim-Renaud, Young-Key (ed.) Theoretical issues in Korean linguistics. Stanford: CSLI. 133–156.Google Scholar
Legendre, Géraldine, Miyata, Yoshiro & Smolensky, Paul (1990). Harmonic Grammar: a formal multi-level connectionist theory of linguistic well-formedness: an application. In Proceedings of the 12th Annual Conference of the Cognitive Science Society. Hillsdale: Erlbaum. 884–891.Google Scholar
McFadden, Daniel (1974). Conditional logit analysis of qualitative choice behaviour. In Zarembka, Paul (ed.) Frontiers in econometrics. New York: Academic Press. 105–142.Google Scholar
Manning, Christopher D. & Schütze, Hinrich (1999). Foundations of statistical natural language processing. Cambridge, Mass: MIT Press.Google Scholar
Parker, Stephen G. (2002). Quantifying the sonority hierarchy. PhD dissertation, University of Massachusetts, Amherst. Available (May 2011) at http://scholarworks.umass.edu/dissertations/AAI3056268.Google Scholar
Pater, Joe (2009). Weighted constraints in generative linguistics. Cognitive Science 33. 999–1035.CrossRefGoogle ScholarPubMed
Pierrehumbert, Janet B. (2006). The statistical basis of an unnatural alternation. In Goldstein, Louis, Whalen, D. H. & Best, Catherine T. (eds.) Laboratory Phonology 8. Berlin & New York: Mouton de Gruyter. 81–106.CrossRefGoogle Scholar
Potts, Christopher, Pater, Joe, Jesney, Karen, Bhatt, Rajesh & Becker, Michael (2010). Harmonic Grammar with linear programming: from linear systems to linguistic typology. Phonology 27. 77–117.CrossRefGoogle Scholar
Prince, Alan & Smolensky, Paul (1993). Optimality Theory: constraint interaction in generative grammar. Ms, Rutgers University & University of Colorado, Boulder. Published 2004, Malden, Mass. & Oxford: Blackwell.Google Scholar
R Development Core Team (2006). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Available (May 2011) at http://www.r-project.org.Google Scholar
Ren, Jie, Gao, Liqun & Morgan, James L. (2010). Mandarin speakers' knowledge of the sonority sequencing principle. Paper presented at the 20th Colloquium on Generative Grammar, Universitat Pompeu Fabra, Barcelona.Google Scholar
Selkirk, Elizabeth (1982). The syllable. In Hulst, Harry van der & Smith, Norval (eds.) The structure of phonological representations. Part 2. Dordrecht: Foris. 337–383.Google Scholar
Selkirk, Elisabeth (1984). On the major class features and syllable theory. In Aronoff, Mark & Oerhle, Richard T. (eds.) Language sound structure. Cambridge, Mass.: MIT Press. 107–136.Google Scholar
Sievers, Eduard (1881). Grundzüge der Phonetik, zur Einführung in das Studium der Lautlehre der indogermanischen Sprachen. Leipzig: Breitkopf & Härtel.Google Scholar
Smolensky, Paul & Legendre, Géraldine (2006). The harmonic mind: from neural computation to optimality-theoretic grammar. 2 vols. Cambridge, Mass.: MIT Press.Google Scholar
Steriade, Donca (1982). Greek prosodies and the nature of syllabification. PhD dissertation, MIT.Google Scholar
Vitevitch, Michael S. & Luce, Paul A. (2004). A Web-based interface to calculate phonotactic probability for words and nonwords in English. Behavior Research Methods, Instruments, and Computers 36. 481–487.CrossRefGoogle ScholarPubMed
Wilson, Colin & Davidson, Lisa (in press). Bayesian analysis of non-native cluster production. NELS 40.Google Scholar
Wright, Richard (2004). A review of perceptual cues and cue robustness. In Hayes, et al. (2004). 34–57.CrossRefGoogle Scholar
Supplementary material: File

Daland et al. supplementary data

Training set

Download Daland et al. supplementary data(File)
File 237.7 KB
Supplementary material: PDF

Daland supplementary material

Supplementary information

Download Daland supplementary material(PDF)
PDF 35 KB