1. Interpreting sonority-projection experiments: the role of phonotactic modeling Bruce Hayes Department of Linguistics UCLA

2. August 19, 2011Hayes, Interpreting sonority projection2 Sonority  No exact phonetic definition, but it plays a major role in phonological patterning.  A typical arrangement of consonants by sonority: glides >> liquids >> nasals >> obstruents

3. August 19, 2011Hayes, Interpreting sonority projection3 Sonority sequencing  Sonority Sequencing Principle (Sievers 1881; Jespersen 1904; Hooper 1976; Steriade 1982; Selkirk 1984).  Sonority preferentially rises through syllable-initial clusters, and falls through syllable-final clusters. Large rises (resp. falls) are better.  A pretty good syllable: [pla]  A very mediocre syllable: [pta] (tied)  A really terrible syllable: [lpa] (reversed)

4. August 19, 2011Hayes, Interpreting sonority projection4 The sonority projection effect  Ask an English speaker: How good a syllable is [lba]? (horrible sonority violation)  How does it compare with [bda]? (merely bad sonority violation)  Idea: [lba] is much worse even though the English speaker never heard either one during language acquisition.

5. August 19, 2011Hayes, Interpreting sonority projection5 The effect emerges in controlled experiments  Example: Daland et al. (2011)  In the following slide:  Horizontal axis: sonority difference of initial cluster, following the theory of Clements (1990)  Vertical axis: ratings by native speakers (victory percentage, all possible pairwise comparisons of the stimuli)

6. August 19, 2011Hayes, Interpreting sonority projection6 Sonority projection in Daland et al. (2011)

7. August 19, 2011Hayes, Interpreting sonority projection7 Earlier experimental literature on sonority projection  English: Pertz and Bever (1975) Albright (2007) Daland et al. (2011) Berent, Steriade, Lennertz, and Vaknin (2007) Berent, Smolensky, Lennertz, and Vaknin-Nusbaum (2009)  Korean: Berent, Lennertz, Jun, Moreno, and Smolensky (2008)  Mandarin: Ren et al. (2010)

8. August 19, 2011Hayes, Interpreting sonority projection8 Three accounts of sonority projection  Universal constraint set (as in Optimality Theory) Not the whole story: how could such a set be deployed in a grammar that derives sonority projection?  Relative phonetic difficulty in the production of bad-sonority clusters See Redford (2008) and work cited there  Generalized from the data the child hears English has /br/, /pl/, not */rb/, */lp/ — could this be enough to distinguish /bd/ from /lb/? This is the possibility pursued here.

9. August 19, 2011Hayes, Interpreting sonority projection9 Research plan  Strategy: computational modeling, using Hayes and Wilson’s (2008) phonotactic learner  Goal: develop grammars that model sonority projection, generalizing from very minimal training data

10. August 19, 2011Hayes, Interpreting sonority projection10 Earlier work modeling English  Daland et al. (2011) use the Hayes/Wilson learner to project sonority, using English learning data.  But sonority projection has been shown for languages with much smaller onset inventories than English – is projection possible in such cases?

11. August 19, 2011Hayes, Interpreting sonority projection11 Bwa and Ba  Bwa is a fictional language whose branching onsets are limited to stop + glide.  Ba is a fictional language with no branching onsets at all.  Goal: show that sonority projection is possible, without stipulating the Sonority Sequencing Principle a priori.

12. August 19, 2011Hayes, Interpreting sonority projection12 Assumptions I: the feature system  From Clements (1990); each feature defines a cutoff on the Sonority Hierarchy. glidesliquidsnasalsobstruents [vocoid]+−−− [approximant]++−− [sonorant]+++−

13. August 19, 2011Hayes, Interpreting sonority projection13 Assumptions II: a “UG” of constraints, all “sonority regulating”  A constraint is sonority-regulating if it looks like one of these:  For initial clusters, half the sonority-regulating constraints are “sensible”, half “silly” – both included.

14. August 19, 2011Hayes, Interpreting sonority projection14 Sample list of constraints *[−syllabic][−sonorant] *[−syllabic][−approximant] *[−syllabic][−vocoid] *[−syllabic][−syllabic] *[+sonorant][−sonorant] *[+sonorant][−approximant] *[+sonorant][−vocoid] *[+approximant][−syllabic] *[+approximant][−continuant] *[+approximant][−sonorant] *[+approximant][−vocoid] *[−consonantal][−sonorant] *[−consonantal][−approximant] *[−consonantal][−vocoid] *[−consonantal][−syllabic] *[−syllabic][+sonorant] *[−syllabic][+approximant] *[−syllabic][−vocoid] *[−sonorant][+sonorant] *[−sonorant][+approximant]

15. August 19, 2011Hayes, Interpreting sonority projection15 Phonemes of Bwa ptkabdgfsvzmnlrwjptkabdgfsvzmnlrwj

16. August 19, 2011Hayes, Interpreting sonority projection16 Features for Bwa  Consonants: as given earlier  Vowels: no sonority features, only [+syllabic]

17. August 19, 2011Hayes, Interpreting sonority projection17 Training data: the full vocabulary of Bwa pa ta ka ba da ga fa sa va za ma na la ra ja wa pwa twa kwa bwa dwa gwa pja tja kja bja dja gja

18. August 19, 2011Hayes, Interpreting sonority projection18 Weighting the constraints  Feed the Hayes/Wilson learner Bwa, with the a priori constraints just given.  Learned a grammar — assigning weights to the constraints  Software used: http://www.linguistics.ucla.edu/people/hayes/ Phonotactics/

19. August 19, 2011Hayes, Interpreting sonority projection19 Testing the learned grammar  Obtain the “penalty scores” it assigns to 16 syllables that embody every possible sonority sequence  Arrows show well- formedness differences that should be observed if sonority is projected. wwawrawmawpa rwarrarmarpa mwamrammampa pwaprapmappa

20. August 19, 2011Hayes, Interpreting sonority projection20 Result  All and only stop + glide clusters perfect.  They served as the empirical “kernel” for successful sonority projection.

21. August 19, 2011Hayes, Interpreting sonority projection21 The weights of the learned grammar for Bwa  All of the sensible sonority-regulating constraints got positive weights.  All of the silly ones got zero.

22. August 19, 2011Hayes, Interpreting sonority projection22 Simulation for the Ba Language  Training data: pa ta ka ba da ga fa sa va za ma na la ra ja wa  Vowels assumed to have sonority — same features as glides  Same as before:  set of possible constraints  training procedure

23. August 19, 2011Hayes, Interpreting sonority projection23 Results for Ba  Again, sonority projection.  This time every cluster is penalized, but differentially.

24. August 19, 2011Hayes, Interpreting sonority projection24 Diagnosing the simulations  Playing with various constraint sets, I found that: For projection to happen, the constraints be sonority-regulating. If you include constraints with all possible sequences of sonority features, you don’t get projection  Why?

25. August 19, 2011Hayes, Interpreting sonority projection25 A very simple sonority hierarchy for diagnosis pmw [sonorant]−++ [vocoid]−−+

26. August 19, 2011Hayes, Interpreting sonority projection26 The sonority-regulating constraints Sensible *[+vocoid][−sonorant] *[+vocoid][−vocoid] *[+vocoid] C *[+sonorant][−sonorant] *[+sonorant][−vocoid] *[+sonorant] C *C [−sonorant] *C [−vocoid] Silly *[−vocoid][+sonorant] *[−vocoid][+vocoid] *[−vocoid] C *[−sonorant][+sonorant] *[−sonorant][+voicoid] *[−sonorant] C *C [+sonorant] *C [+vocoid]

27. August 19, 2011Hayes, Interpreting sonority projection27 The region of possible clusters for Bwa (examples) *ww*wm*wp *mw*mm*mp ✓ pw*pm*pp

28. August 19, 2011Hayes, Interpreting sonority projection28 What is banned by sensible sonority- regulating constraints? -- “Upward L’” *[+sonorant] C *C [−vocoid] (plus 7 more) *ww*wm*wp *mw*mm*mp ✓ pw*pm*pp

29. August 19, 2011Hayes, Interpreting sonority projection29 All “silly” constraints ban a legal cluster (total of 9; not all shown) *ww *wm *wp *mw *mm *mp ✓ pw *pm *pp

30. August 19, 2011Hayes, Interpreting sonority projection30 What happens when the constraints are weighted?  All of the silly constraints forbid ✓ [pw] – so they get zero weight.  Two sensible constraints together, *[+sonorant]C and *C[−vocoid], could do all the work—and maxent does give them the greatest weights.  But the system is cautious—Gaussian prior penalizes big weights on individual constraints  So, descriptive burden is shared among the other sensible constraints.  Basis of sonority projection: the worse the sonority sequencing of the cluster, the more sensible constraints it violates.

31. August 19, 2011Hayes, Interpreting sonority projection31 What have we got?  An explicit grammar that (qualitatively) matches sonority projection intuitions  Learning of the grammars weights from very minimal information: the sonority drop across /bw/ (in Bwa), across /ba/ in (Ba)

32. August 19, 2011Hayes, Interpreting sonority projection32 But learning still depends on much a priori knowledge  Features that regulate sonority  Restriction of sonority constraints to “sonority- regulating ones”  Nothing said yet about codas, where sonority rises The system must be told to look at the syllable peripheries, so it will generalize properly. See full version of this paper.

33. August 19, 2011Hayes, Interpreting sonority projection33 Thank you  Full paper is available in conference proceedings and on line at http://www.linguistics.ucla.edu/people/hayes  Author email: bhayes@humnet.ucla.edu