Fruehwald, Josef

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     Edinburgh Research Explorer                                      Gender effects on inter and intra-speaker variance in soundchangeCitation for published version:Fruehwald, J 2017, 'Gender effects on inter and intra-speaker variance in sound change' Paper presentedat New Ways of Analyzing Variation 46 (NWAV46), Madison, WI, United States, 2/11/17 - 5/11/17, .Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Publisher's PDF, also known as Version of recordGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 05. Apr. 2019Gender effects on inter and intra-speaker variance in sound changeJosef FruehwaldThe Dynamics of Sound ChangeThe study of vowel shifts has largely focused on shifts in the average value of vowels in the Hz space.Labov (2001)The Dynamics of Sound ChangeBut other distributional properties have always been of some interest.Labov, Baranowski, Dinkin (2010)Sharma (2011)Leaders & LaggardsSankoff & Blondeau (2007)Outliers Stylistic RangeVan Hofwegen (2015)The Dynamics of Sound ChangeThey play an important role in sound change theory more broadly.Error Accumulation Convergence ModelOne Speaker’s RepresentationSpeakers in a CommunityOhala (1981, among others) Baker et al (2011)Blevins (2004)The Dynamics of Sound Change/ay/ /ey/[ayT] — Raised[ayD] — Stayed the same[eyC] — Raised[eyV] — Stayed the sameThere were no substantial changes to variance parameters over the course of the change for allophones that changed compared to those that didn’t.I’ve previously investigated language internal effects on variance parameters:Speakers stayed similarly clustered wrt to each other.Speakers had similar ranges of use.Gender…Gender© Mark Hooper https://flic.kr/p/aebrhiDistributional Properties and GenderLeaders and Laggards in Sound ChangeSociolinguistic Change? (Coupland 2009)This is a largely exploratory analysis, but it has a few clear implications for sound change, genderTo investigate these effects, I contrast a strongly gendered sound change with a weakly gendered sound change.Women tend to lead sound change.Flamboyant personae (Eckert, 2011) = larger variance?As changes progress, does it affect speakers stylistic range?Do the ways of being a man or a woman wrt to the linguistic performance change with sound change?The Data❖ Data drawn from the Philadelphia Neighborhood Corpus❖ 325 White speakers from Philadelphia❖ Interviewed between 1973 and 2013❖ Dates of Birth between 1888 and 1998❖ Sociolinguistic interviews that have been transcribed & FAVEd.❖ 2 Changes❖ ay0 — pre-voiceless /ay/ raising❖ aw — /aw/ raising and fronting The Sound Changes/ay0/19,383 tokens325 PNC SpeakersRecently, men’s backer realizations associated with masculinity and toughness. (Conn, 2005; Wagner, 2007)The Sound Changes/aw/6,720 tokens321 PNC SpeakersWomen have both the raising and fronting of /aw/ and then the subsequent reversal.Modeling VarianceStandard LMMs Bayesian ModelingGenerate inter-speaker variance estimates Can’t vary wrt other variablesAssume a constant intra-speaker variance estimateExceptionally flexible in what they can estimate.Researcher must fully define the model.They must be explained in detailFor this project, I’ve defined a custom Bayesian Model implemented in Stan.The Modelμ σEither an intra-speakerOr inter-speaker distributionThe Modelδiμiδi+1μi+1 = μi + δiμi+2 = μi+1 + δi+1δi ~ normal(δi-1, σ)“Autoregression”μi  = the estimated mean for each year of birthδi  = the difference from the prior yearIf δi = 0, no changeIf δi > 0, increaseIf δi < 0, decreaseThe Modelσiσi+1 = σi × eδiσi+2 = σi+1 × eδi+1δi ~ normal(δi-1, σ)σi  = the estimated variance“Autoregression”If δi = 0, no changeIf δi > 0, increaseIf δi < 0, decreaseeδi = how many times the previous yearThe ModelsIntra-Speaker Model Inter-Speaker ModelWithin speaker variances are estimated as an AR process, and can vary over DOBBetween speaker variances are estimated as a fixed parameter.Within speaker variances are estimated as a fixed parameter.Between speaker variances are estimated as an AR process and can vary over DOBIt’s too complex to model both intra-speaker and inter-speaker variances using an AR process at the same time.Both models: The estimated average for DOB cohorts is estimated as an AR process.Random intercepts by wordIntra-Speaker Results: μ0.00.51.01.51890 1920 1950 1980Date of BirthAverage Normalized F1genderwomenmenpre−voiceless /ay/−2.0−1.5−1.0−0.51890 1920 1950 1980Date of BirthAverage Normalized F2−F1genderwomenmen/aw/Estimated averages by DOB and gender.The trends are what we would expect from earlier work (Labov et al 2013)The fuzziness of the boundaries indicate that we actually get out probability distributions from the model.Intra-Speaker Results: σThese are how the intra-speaker variance is estimated to change over time.0.51.01890 1920 1950 1980Date of BirthNormalized F1 Variancegenderwomenmenpre−voiceless /ay/ (within speaker)0.51.01.51890 1920 1950 1980Date of BirthNormalized F2−F1 Variance genderwomenmen/aw/ (within speaker)There is no gender effect on /ay/. There is weak evidence for a gender effect on /aw/Intra-Speaker Results: σ0.51.01.52.01890 1920 1950 1980dobmen's sigma multiplier/ay/ gender effect (within speaker)0.00.51.01.52.01890 1920 1950 1980dobmen's sigma multiplier/aw/ gender effect (within speaker)This is estimated difference between the curves. Where the bands include 1, there isn’t a robust difference.Intra-Speaker Results❖ The range of intra-speaker variation looks remarkably similar for /ay/ and /aw/, despite looking very different in their overall means.❖ The evidence for gendered differences in intra-speaker range (style, etc.) is limited,❖ The evidence for changes in intra-speaker range (gendered or otherwise) is limited.❖ These results may be limited by the (non)range of linguistic activities these speakers are engaged in.Inter-Speaker Results: μ0.00.51.01.51890 1920 1950 1980Date of BirthAverage Normalized F1genderwomenmenpre−voiceless /ay/−2.0−1.5−1.0−0.51890 1920 1950 1980Date of BirthAverage Normalized F2−F1genderwomenmen/aw/Estimated averages by DOB and gender.These are just here for a sanity check that the model is estimating the parameters well.Inter-Speaker Results: σ0.00.20.40.60.81890 1920 1950 1980Date of BirthNormalized F1 Variancegenderwomenmenpre−voiceless /ay/ (between speaker)0.00.30.60.91890 1920 1950 1980Date of BirthNormalized F2−F1 Variance genderwomenmen/aw/ (between speaker)For /ay/ there is a stronger trend for men to be more tightly clustered together, which is amplified at the same time the change tapers off for women.For /aw/, there is better evidence that women are less tightly clustered together than men.Inter-Speaker Results: σ0.00.51.01.51890 1920 1950 1980dobmen's sigma multiplier/ay/ gender effect (between speaker)0121890 1920 1950 1980dobmen's sigma multiplier/aw/ gender effect (between speaker)For /ay/, there isn’t strong evidence for gendered differences in inter-speaker range till late in the 20th century (at the same time the change levels off for women).For /aw/, there is reliable gendered differences in inter-speaker range relatively early, but it’s also fairly stable, unconnected to the patterns in the means.Inter-Speaker Results❖ If there is a consistency in these results, it is that there is a narrower range of inter-speaker variation for men than for women.❖ Could be directly due to differently available range in gender performance.❖ Could be due to differences in the nature and constitution of social networks. (Dodsworth, ~1 hour from now?)❖ This narrowing of inter-speaker range appears to develop later for /ay/.❖ Possibly coincides with the association of backed [ay0] with masculinity and toughness (Conn 2005, Wagner 2007).❖ The size of inter-speaker range doesn’t seem to be related to who is leading the sound change in any way.Inter-Speaker Results0.00.51.01.51890 1920 1950 1980Date of BirthNormalized F1genderwomenmen−1.5−1.0−0.50.01890 1920 1950 1980Date of BirthNormalized F2 − F1genderwomenmenThis is what the estimated inter-speaker distributions actually look like.Conclusions❖ Despite having very similar intra-speaker ranges to each other, women appear to have a broader range of inter-speaker averages than men.❖ The narrowing of the inter-speaker range for /ay/ is open to a number of interpretations:❖ Association of /ay/ with toughness focuses men’s averages around a dimension of gender expression they were already narrowly arranged along?❖ Perhaps there has been a sociolinguistic change, eliminating a possible way of doing /ay0/❖ These (non)shifts in variance don't seem to be tightly connected to the shifts in the averages.❖ Variance modeling can uncover interesting trends, and some surprising results.Thanks!The Intra-Speaker ModelAR Processhalf-cauchyμnormalσμnormalσAR Processhyperpriorspriorsspeaker-leveldistributionsobserved dataDOB average changesSpeakers remain similarly clusteredIndividual speaker variability changes with DOBThe Inter-Speaker ModelAR Processhalf-cauchyμnormalσμnormalσAR Processhyperpriorspriorsspeaker-leveldistributionsobserved dataThis time, inter-speaker variability varies by DOB Each speaker has a σ, but this doesn’t vary in any interesting way 

Gender effects on inter and intra-speaker variance in sound change

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Gender effects on inter and intra-speaker variance in sound change

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