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Lexical competition, frequency and the fronting of back vowels in Northern British Englishes

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Hughes, V., Foulkes, P., Haddican, B. and LaShell, P. (2013) Lexical competition, frequency and the fronting of back vowels in Northern British Englishes. Paper presented at UKLVC9 Conference, University of Sheffield. 2-4 September 2013.

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Lexical competition, frequency and the fronting of back vowels in Northern British Englishes

  1. 1. Lexical competition, frequency and the fronting of back vowels in Northern British Englishes Vincent Hughes (University of York) Paul Foulkes (University of York) Bill Haddican (CUNY – Queens College) Pat LaShell (NZILBB, University of Canterbury) A Comparative Study of Language Change in Northern Englishes (2008-13) ESRC: RES-061-25-0033
  2. 2. 1. Introduction • preservation of contrast – guiding principle in language variation and change – chain shift model (Labov 1994) • mergers are more likely where functional load is low (Martinet 1955) – phonemes maximally phonetically distinct • primarily associated with the phonological level • but these principles don’t exclude lexical factors: – in fact they make predictions about lexical factors – plenty of evidence from Hay (yesterday!) 2 Hughes, Foulkes, Haddican & LaShell UKLVC9
  3. 3. 1. Introduction predictability attracts innovation (à change?) • high frequency words – more deletion of /-t,d/ (Bybee 2001) – [V] centralisation (Munson & Solomon 2004, Aylett & Turk 2006) 3 Hughes, Foulkes, Haddican & LaShell UKLVC9
  4. 4. 1. Introduction predictability attracts innovation (à change?) • high frequency words – more deletion of /-t,d/ (Bybee 2001) – [V] centralisation (Munson & Solomon 2004, Aylett & Turk 2006) role of lexical factors largely dismissed by e.g.: • Labov (2003): GOAT-fronting in North America – non-reductive changes show “no effect of word frequency” 4 Hughes, Foulkes, Haddican & LaShell UKLVC9
  5. 5. 1. Introduction predictability attracts innovation (à change?) • high frequency words – more deletion of /-t,d/ (Bybee 2001) – [V] centralisation (Munson & Solomon 2004, Aylett & Turk 2006) • repetition: – probabilistic reduction theory (Jurafsky et al 2000) • high frequency words & repetitions – more /t/ à [d] (Foulkes & Hay tomorrow) 5 Hughes, Foulkes, Haddican & LaShell UKLVC9
  6. 6. 1. Introduction confusability • lexical competition (i.e. minimal pairs) inhibits change some minimal pairs are more confusable than others: • phonetic distance – goose~geese more confusable than goose~gas • syntactic category – Wedel et al (2013): merger only genuinely threatened by lexical items of the same syntactic category 6 Hughes, Foulkes, Haddican & LaShell UKLVC9
  7. 7. 1. Introduction predictions Hughes, Foulkes, Haddican & LaShell UKLVC9 7 competitor frequency high low target word frequency high ? fast low slow ?
  8. 8. 1. Introduction predictions Hughes, Foulkes, Haddican & LaShell UKLVC9 8 competitor frequency high low target word frequency high ? fast low slow ?
  9. 9. 2.1 Present study • worldwide fronting of GOOSE and GOAT – “off the shelf” change (Milroy 2007, Haddican et al 2013) – Labov (1994): Principle III of vowel change • some consistent patterns of variation – social: led by young speakers (GOOSE) and women (GOAT) (Hall-Lew 2004, Baranowski 2008, etc) – GOAT fronting parasitic on GOOSE fronting – phonological: post-/j/ (higher F2), pre-/l/ (lower F2) Hughes, Foulkes, Haddican & LaShell UKLVC9 9
  10. 10. 2.1 Present study • fronting leads to potential for merger goose~geese, coke~cake, June~Jean – but fronting can also lead to greater separation of phonemes goon~gun, coke~cook – where the phoneme started and where it is going should affect the predictions we might make about individual lexical items Hughes, Foulkes, Haddican & LaShell UKLVC9 10
  11. 11. 2.1 Present study research questions • what is the role of lexical factors in explaining a chain shift in progress? - relatively little work on the role of lexical factors in change which is not reductive • how do the opposing forces of frequency, functional load, and competition interact? - are some factors more important than others? Hughes, Foulkes, Haddican & LaShell UKLVC9 11
  12. 12. York Manchester c. 40 tokens per speaker per vowel 2.2 Data Young (18-22) Older (59-78) Male 8 8 Female 10 8 12 Young (18-21) Older (62-82) Male 8 8 Female 8 8 Hughes, Foulkes, Haddican & LaShell UKLVC9
  13. 13. 2.2 Data • time-normaliseddynamic measurements of F1 and F2 trajectories for GOOSE and GOAT (McDougall 2004) 13 • normalisedusing modified Watt and Fabricius method using vowels package in R • reference vowels: FLEECE, START, THOUGHT
  14. 14. 3. Method: predictors frequency of target word • R used to generate target frequency information from CELEX British English lexical database: - frequency transformed using natural logarithm 14 Hughes, Foulkes, Haddican & LaShell UKLVC9
  15. 15. 3. Method: predictors competitors (minimal pairs) • R used to generate possible competitors (minimal pairs) for each target item from CELEX: – competitors from all lexical sets included – implausible competitors removed (e.g. blae) – potential competitors added (e.g. proper names) • sum of competitor frequencies across all lexical sets containing minimal pairs with target 15 Hughes, Foulkes, Haddican & LaShell UKLVC9
  16. 16. 3. Method: predictors 16 Hughes, Foulkes, Haddican & LaShell UKLVC9 • low frequency = 1st quartile • high frequency = 4th quartile low/low low/high high/low high/high boobs boot both most target frequency 15 179 10930 21314 all competitor freqs 47 106631 2069 17682 N words 5 17 5 8 N phonemes 5 12 3 6
  17. 17. 3. Method: lmer model (lme4) • dep. variable – point of maximum F2 • random intercepts – speaker – lexical root • different models fitted for GOOSE/ GOAT in Manchester and York 17 Hughes, Foulkes, Haddican & LaShell UKLVC9
  18. 18. 4.1 Results: competition • starting point: maintenance of contrast is central to variation and change GOOSE GOAT Competitors 108/330 32.7% 171/380 45% No competitors 222/330 67.3% 209/380 55% Hughes, Foulkes, Haddican & LaShell UKLVC9 18
  19. 19. 4.1 Results: competition • lexical items with competitors extracted from dataset • competitor words separated into phonetically ‘back’ and ‘non-back’ categories Hughes, Foulkes, Haddican & LaShell UKLVC9 19
  20. 20. 4.1 Results: competition Hughes, Foulkes, Haddican & LaShell UKLVC9 GOOSE GOAT FOOT THOUGHT • fronting promoted with: - high frequency ‘back’ competitors - low frequency ‘non-back’ competitors 20
  21. 21. 4.1 Results: competition Hughes, Foulkes, Haddican & LaShell UKLVC9 GOOSE KIT FLEECE DRESS NURSE • fronting inhibited: - high frequency ‘non-back’ competitors 21
  22. 22. 4.1 Results: competition • lexical items with competitors extracted from dataset • competitor words separated into phonetically ‘back’ and ‘non-back’ categories input predictors • frequencies summed across ‘back’ and ‘non-back’ competitors • frequency ratio of ‘back’ : ‘non-back’ competitors • high = higher frequency of ‘back’ competitors • low = higher frequency of ‘non-back’ competitors Hughes, Foulkes, Haddican & LaShell UKLVC9 22
  23. 23. 4.1 Results: competition • effects not statistically significant for GOOSE or GOAT in Manchester or York - BUT approaching significance in 3 of 4 models - effect in predicted direction • t-values: - Manchester GOOSE = 0.59 - York GOOSE = 1.86 - Manchester GOAT = 1.995 - York GOAT = 1.73 (threshold for significance: t ≥ 2) Hughes, Foulkes, Haddican & LaShell UKLVC9 23
  24. 24. 4.2 Experiment 2: interaction • considering the interaction between ‘predictability’ and ‘confusability’ • all of the data used in the analysis input predictors • standardisedlog target frequency • sum of all standardisedlog competitor frequencies 24 Hughes, Foulkes, Haddican & LaShell UKLVC9
  25. 25. 4.2 Experiment 2: interaction GOAT • no significant effects for tar freq, summed comp freq or the interaction between them – both in Manchester and York Hughes, Foulkes, Haddican & LaShell UKLVC9 25
  26. 26. 4.2 Experiment 2: interaction Estimate Std. Error t value (Intercept) 1.452 0.046628 31.131 summed competitor frequency -0.036 0.009826 -3.662 target frequency -0.002 0.006351 -0.325 comp freq:target frequency 0.003 0.001034 3.15 Manchester GOOSE • main effect for summed competitor frequency • interaction between competitor frequency and target frequency Hughes, Foulkes, Haddican & LaShell UKLVC9 26
  27. 27. -1.0 -0.5 0.0 0.5 1.0 1.5 1.251.301.351.40 Competitor Frequency NormalisedF2(+10%step) TargetFrequency Low Frequency Mean Frequency High Frequency Manchester GOOSE Low Frequency Mean Frequency High Frequency FRONT BACK LOW COMP FREQ HIGH COMP FREQ
  28. 28. 4.2 Experiment 2: interaction Estimate Std. Error t value (Intercept) 1.289 0.0383466 33.63 summed competitor frequency -0.038 0.0080621 -4.75 target frequency 0.007 0.0051652 1.29 comp freq:target frequency 0.003 0.0009169 3.56 York GOOSE • main effect for summed competitor frequency • interaction between competitor frequency and target frequency Hughes, Foulkes, Haddican & LaShell UKLVC9 28
  29. 29. -1.0 -0.5 0.0 0.5 1.0 1.5 1.101.151.201.251.301.35 Competitor Frequency NormalisedF2(+10%step) TargetFrequency Low Frequency Mean Frequency High Frequency York GOOSE Low Frequency Mean Frequency High Frequency FRONT BACK LOW COMP FREQ HIGH COMP FREQ
  30. 30. 5. Discussion predictions Hughes, Foulkes, Haddican & LaShell UKLVC9 30 competitor frequency high low target word frequency high ? fast low slow ?
  31. 31. 5. Discussion summary of findings Hughes, Foulkes, Haddican & LaShell UKLVC9 31 competitor frequency high low target word frequency high gradient fast low slow fast
  32. 32. 5. Discussion 32 Hughes, Foulkes, Haddican & LaShell UKLVC9 • in both experiments neither ‘predictability’ nor ‘confusability’ factors alone explain variation in the data – need to think about opposing pressures on the system – lexical factors go beyond target frequency or functional load • clear effects for GOOSE in both cities: – low frequency competitors = higher F2 (regardless of tar freq) – high comp frequency = lower F2 – Effect much stronger for low frequency target items
  33. 33. 5. Discussion 33 • no frequency effects for GOAT in either data set – issues with the dep variable for GOAT – social baggage of GOAT in York (Haddican et al 2013) and Manchester (Drummond, today)? – more competitors for GOAT (more stability in the system)? • magnitude of the effects for lexical factors much smaller than those for social and phonological factors (Hughes et al 2012) – this doesn’t mean lexical factors are not relevant… Hughes, Foulkes, Haddican & LaShell UKLVC9
  34. 34. 6. Conclusion 34 Hughes, Foulkes, Haddican & LaShell UKLVC9 • maintenance of contrast appears to be playing a role in promoting/ inhibiting change - more fronting when frequency of ‘back’ competitors is higher than frequency of ‘non-back’ competitors • lexical factors involve both ‘predictability’ and ‘confusability’ – words with frequent competitors have lower F2 than those with less frequent competitors – this effect is strongest where the target item is low frequency
  35. 35. Thanks to: Participants in Manchester and York, Nathan Atkinson, Laura Bailey, Diane Blakemore, Dan Johnson, Jen Hay, Holly Prest, Sali Tagliamonte, Dominic Watt, Sophie Wood, audiences at UKLVC8, CUNY, Sociolinguistics Symposium, NWAV 41 A Comparative Study of Language Change in Northern Englishes (2008-13) ESRC: RES-061-25-0033 Thanks! Questions? vh503@york.ac.uk

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