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Using hidden Markov models to find discrete targets in continuous sociophonetic data

  • Daniel Duncan ORCID logo EMAIL logo
Veröffentlicht/Copyright: 27. Juli 2021
Linguistics Vanguard
Aus der Zeitschrift Linguistics Vanguard Band 7 Heft 1

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.

Keywords: hidden Markov models; quantitative methods; sociophonetics; variation
Corresponding author: Daniel Duncan, School of English Literature, Language and Linguistics, Newcastle University, Percy Building, Newcastle upon Tyne NE1 7RU, UK, E-mail:

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.

  1. 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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Received: 2020-06-22
Accepted: 2020-11-09
Published Online: 2021-07-27

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Editorial Note
  2. Editorial note
  3. Phonetics & Phonology
  4. Fast Track: fast (nearly) automatic formant-tracking using Praat
  5. Acoustic investigation of anticipatory vowel nasalization in a Caribbean and a non-Caribbean dialect of Spanish
  6. Evidence against a link between learning phonotactics and learning phonological alternations
  7. The extent and degree of utterance-final word lengthening in spontaneous speech from 10 languages
  8. Morphology & Syntax
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  13. Semantics & Pragmatics
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  18. Experimental filler design influences error correction rates in a word restoration paradigm
  19. Phonological and morphological roles modulate the perception of consonant variants
  20. Language Acquisition and Language Learning
  21. Sounds like a dynamic system: a unifying approach to Language
  22. Sociolinguistics and Anthropological Linguistics
  23. Using hidden Markov models to find discrete targets in continuous sociophonetic data
  24. “It’s a Whole Vibe”: testing evaluations of grammatical and ungrammatical AAE on Twitter
  25. The sociolinguistics of /l/ in Manchester
  26. Computational & Corpus Linguistics
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  28. A computational construction grammar approach to semantic frame extraction
  29. The “negative end” of change in grammar: terminology, concepts and causes
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  31. Cognitive Linguistics
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https://doi.org/10.1515/lingvan-2020-0057
Schlagwörter für diesen Artikel
hidden Markov models; quantitative methods; sociophonetics; variation

Artikel in diesem Heft

  1. Editorial Note
  2. Editorial note
  3. Phonetics & Phonology
  4. Fast Track: fast (nearly) automatic formant-tracking using Praat
  5. Acoustic investigation of anticipatory vowel nasalization in a Caribbean and a non-Caribbean dialect of Spanish
  6. Evidence against a link between learning phonotactics and learning phonological alternations
  7. The extent and degree of utterance-final word lengthening in spontaneous speech from 10 languages
  8. Morphology & Syntax
  9. Brand names as multimodal constructions
  10. NP-internal structure and the distribution of adjectives in Mə̀dʉ́mbὰ
  11. A quantitative investigation of the ellipsis of English relativizers
  12. Positional dependency in Murrinhpatha: expanding the typology of non-canonical morphotactics
  13. Semantics & Pragmatics
  14. Multifactorial Information Management (MIM): summing up the emerging alternative to Information Structure
  15. Language Documentation & Typology
  16. Current trends in grammar writing
  17. Psycholinguistics & Neurolinguistics
  18. Experimental filler design influences error correction rates in a word restoration paradigm
  19. Phonological and morphological roles modulate the perception of consonant variants
  20. Language Acquisition and Language Learning
  21. Sounds like a dynamic system: a unifying approach to Language
  22. Sociolinguistics and Anthropological Linguistics
  23. Using hidden Markov models to find discrete targets in continuous sociophonetic data
  24. “It’s a Whole Vibe”: testing evaluations of grammatical and ungrammatical AAE on Twitter
  25. The sociolinguistics of /l/ in Manchester
  26. Computational & Corpus Linguistics
  27. An empirical study on the contribution of formal and semantic features to the grammatical gender of nouns
  28. A computational construction grammar approach to semantic frame extraction
  29. The “negative end” of change in grammar: terminology, concepts and causes
  30. In order that – a data-driven study of symptoms and causes of obsolescence
  31. Cognitive Linguistics
  32. Iconicity ratings really do measure iconicity, and they open a new window onto the nature of language
  33. Iconicity ratings really do measure iconicity, and they open a new window onto the nature of language
  34. Repetition in Mandarin-speaking children’s dialogs: its distribution and structural dimensions
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