Mergers in Production and Perception Part 2

University:
Boston University
Course:
QST AC 431 | Sustainability Reporting
Academic year:

2024
Views:

41

Pages:

17
Author:

StellarStrategist

MALE 7: REAL WORDS 2400 2200 2000 1800 F2 1600 1400 1200 1000 300 HEAD 400 500 TRAP 600 HAD 700 800 900 F1 DRESS MALE 7: NONSENSE WORDS 2400 2200 2000 1800 F2 1600 1400 1200 1000 300 HEAD 400 500 TRAP 600 HAD 700 800 900 F1 DRESS FEMALE 8: REAL WORDS F2 2800 2600 2400 2200 2000 1800 1600 1400 1200 1000 300 400 500 HEAD TRAP HAD F1 600 DRESS 700 800 900 1000 1100 FEMALE 8: NONSENSE WORDS 2800 2600 2400 2200 2000 F2 1800 1600 1400 1200 1000 300 400 500 HEAD TRAP HAD 700 800 900 1000 1100 F1 600 DRESS This interacts with social class higher SEC lower SEC In Percep.on • Some listeners were more accurate at iden.fying vowels in real words than nonsense words. Summary of results • Some NZers were more accurate with real words than nonsense words in percep.on… • But they maintained a greater dis.nc.on with nonsense words than real words in produc.on. Interpreta.on • In produc.on, speakers must rely on phoneme-‐ level produc.ons for nonsense words. • Phonemic representa.ons also contain the non-‐ prelateral (non-‐merged) tokens, which pulls the distribu.ons apart. • In percep.on, the real word exemplars include *some* dis.nct tokens, which help in iden.ﬁca.on (even though the listeners feel they are guessing) Cot/Caught Experiment (Drager et al. in progress) • Same experiment design but with the cot-‐ caught merger • hock • pod • dodd hawk pawed dawd • Par.cipants from Hawai‘i and western states in the con.nental US – regions where we would expect the merger in at least some phonological contexts (Labov et al.) • All par.cipants were merged to at least some degree, especially before /n/. • Par.cipants merged to varying degrees in other phonological contexts: some merged en.rely on LOT whereas others produced the “correct” vowel in over 65% of tokens. Example of a “nearly merged” par.cipant pod pawed Example of a “nearly merged” par.cipant vod vawd Summary of results (so far) • Those who maintain some dis.nc.on were more accurate at iden.fying real words than nonsense words in percep.on (p<0.05). In produc.on, these par.cipants were more likely to maintain a dis.nc.on in nonsense words than in real words (p<0.01). • Those who were fully merged were more accurate at iden.fying nonsense words than real words in percep.on (p<0.05). They did not produce a dis.nc.on in real or nonsense words (though this is based on auditory analysis – acous.c analysis is in progress). Summary of produc.on and percep.on sound task eﬀect for fully merged Ellen/Allan Iden.ﬁca.on n/a Produc.on n/a Iden.ﬁca.on more accurate with nonse words merged in both cot/caught Produc.on eﬀect for condi*onal/nearly merged slightly less accurate with nonse words greater dis.nc.on with nonse words less accurate with nonse words greater dis.nc.on with nonse words In produc.on… • Speakers produce the merger because they are accessing phone.cally-‐rich representa.ons of the words. • Except when they are producing novel words (i.e., when they don’t have phone.cally-‐rich lexical/u_erance-‐level representa.ons to rely on). Then, speakers with some degree of dis.nc.on in some real words/phonological contexts are biased by those dis.nct distribu.ons and are more likely to produce a dis.nc.on. In an iden.ﬁca.on task… • Individuals access phone.cally-‐rich lexical informa.on, enabling their high accuracy rates despite their feeling that they are guessing. – Feel like the words are “the same” because they are linked to the same phonemic label, but – The word-‐level distribu.ons are not completely overlapping in their representa.ons, so they perform above chance during iden.ﬁca.on. • Par.cipants who are fully merged may be more accurate with nonsense words because: – they have adopted a spelling-‐based strategy; par.cipants tune in to some meaningless-‐to-‐them phone.c diﬀerence and assign a spelling to it, or – there may be a greater phone.c dis.nc.on in the cot-‐caught nonsense s.muli than the real words – more analysis and more data are required In sum • There are diﬀerent levels of mental representa.ons. • Diﬀerent tasks (e.g., real vs. nonsense words, produc.on and percep.on tasks) focus on diﬀerent levels of representa.on. • Mergers-‐in-‐progress are the ideal medium in which to explore these ques.ons precisely because their mental representa.ons diﬀer across the diﬀerent levels.