Abstract
Advances in sociophonetic research resulted in features once sorted into discrete bins now being measured continuously. This has implied a shift in what sociolinguists view as the abstract representation of the sociolinguistic variable. When measured discretely, variation is variation in selection: one variant is selected for production, and factors influencing language variation and change are influencing the frequency at which variants are selected. Measured continuously, variation is variation in execution: speakers have a single target for production, which they approximate with varying success. This paper suggests that both approaches can and should be considered in sociophonetic analysis. To that end, I offer the use of hidden Markov models (HMMs) as a novel approach to find speakers’ multiple targets within continuous data. Using the lot vowel among whites in Greater St. Louis as a case study, I compare 2-state and 1-state HMMs constructed at the individual speaker level. Ten of fifty-two speakers’ production is shown to involve the regular use of distinct fronted and backed variants of the vowel. This finding illustrates HMMs’ capacity to allow us to consider variation as both variant selection and execution, making them a useful tool in the analysis of sociophonetic data.
Funding source: NSF
Award Identifier / Grant number: BCS-1651102 DDRI
Acknowledgments
This work was previously presented at the 2019 Symposium on Representations, Usage and Social Embedding in Language Change, held at the University of Manchester. Thanks to the audience there, as well as two anonymous reviewers, for helpful comments.
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Research funding: The data discussed here were collected as part of NSF grant BCS-1651102 DDRI.
Appendix: Example R code
In this study, I use the depmixS4 package (Visser and Speekenbrink 2010) to run hidden Markov models in R (R Core Team 2017). Here, I illustrate the code used to obtain models similar to those run in the study. The 1-state model generated by this code assumes the data to be normally distributed around the mean, while the 2-state model assumes both states are normally distributed around the state mean.
After installing the package, it must be loaded prior to use.
Data should be loaded in one’s preferred format. If the original data file has multiple phones in it, create a new data frame composed of a single-phone subset of the original.
Because there is some randomness involved in an HMM, set the random seed to ensure consistency between runs.
HMMs will be created for individual speakers. For each speaker, make an individual-level subset of the data.
Now make a 2-state HMM for each individual. ‘nstates’ determines the number of states the model assumes. While the model here simply assumes a normal distribution around the state mean, note that the formula can be adapted for more complex modeling if necessary.
In order to view the summary data, we fit the HMM to our data. Viewing the fitted model gives the model AIC, BIC, and log likelihood.
We now make a 1-state HMM and follow the same process.
In this example, the 2-state model is selected because it has the lower BIC. In this case, we run the following to view the initial state probabilities, transition matrix, and response parameters.
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