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Stop contrast acquisition in child Kriol: Evidence of stable transmission of phonology post Creole formation

Published online by Cambridge University Press:  26 July 2023

Rikke L. BUNDGAARD-NIELSEN*
Affiliation:
MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Australia
Brett J. BAKER
Affiliation:
School of Languages and Linguistics, University of Melbourne, Australia
Elise A. BELL
Affiliation:
Department of Linguistics, University of California, Los Angeles, USA
Yizhou WANG
Affiliation:
School of Languages and Linguistics, University of Melbourne, Australia
*
Corresponding author: Rikke L. Bundgaard-Nielsen; Email: rikkelou@gmail.com
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Abstract

Many Aboriginal Australian communities are undergoing language shift from traditional Indigenous languages to contact varieties such as Kriol, an English-lexified Creole. Kriol is reportedly characterised by lexical items with highly variable phonological specifications, and variable implementation of voicing and manner contrasts in obstruents (Sandefur, 1986). A language, such as Kriol, characterised by this unusual degree of variability presents Kriol-acquiring children with a potentially difficult language-learning task, and one which challenges the prevalent theories of acquisition. To examine stop consonant acquisition in this unusual language environment, we present a study of Kriol stop and affricate production, followed by a mispronunciation detection study, with Kriol-speaking children (ages 4-7) from a Northern Territory community where Kriol is the lingua franca. In contrast to previous claims, the results suggest that Kriol-speaking children acquire a stable phonology and lexemes with canonical phonemic specifications, and that English experience would not appear to induce this stability.

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Article
Creative Commons
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press
Figure 0

Table 1. The obstruent inventory of (Roper) Kriol, adapted from Baker et al. (2014)

Figure 1

Table 2. Word-initial VOT (in ms) in Australian English from Sydney (Antoniou et al., 2010), Melbourne (Clothier & Loakes, 2018), and Katherine (Jones & Meakins, 2013), as well as (Roper) Kriol (Baker et al., 2014). Not all studies record values from every place of articulation. N = number of participants

Figure 2

Figure 1. Map of Australia, indicating the location of Beswick, in the Northern Territory (NT).

Figure 3

Table 3. Full list of Kriol target words in IPA, with glosses in English. Target consonant in bold. * An unintended five instances of ‘orange juice’ resulted in the collection of five /dʒ/ tokens, which we have included in our discussion of the results, though no statistical comparison was possible. Stress (from our auditory impressions) and inferred syllable breaks also indicated.

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Figure 2. Examples of the pictures used in the elicitation task (door, table, apple and balloon).

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Figure 3. Boxplots showing distributions of word-initial VOTs, and word-medial VOTs and constriction durations (CD) in voiceless and voiced stops by four places of articulation (labial, alveolar, post-alveolar, and velar), in ms.

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Table 4. Summary of fitted linear mixed-effects models (LMMs) for word-initial voice onset times (VOTs) in child Kriol

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Table 5. Summary of fitted linear mixed-effects models (LMMs) for word-medial voice onset times (VOTs) in child Kriol. Post-alveolars are excluded due to limited observations

Figure 8

Table 6. Summary of fitted linear mixed-effects models (LMMs) for word-medial constriction durations in child Kriol production

Figure 9

Figure 4. Individual mean VOTs of voiced and voiceless stops in initial position. The numbers above each child’s mean values in the figure indicate the difference in VOT between the voiceless and the voiced stops. Children are coded based on their age (i.e., KC01 was the youngest and KC13 was the oldest).

Figure 10

Figure 5. Individual mean VOTs of voiced and voiceless stops in medial position. The numbers above each child’s mean values in the figure indicate the difference in VOT between the voiceless and the voiced stops. Children are coded based on their age (i.e., KC01 was the youngest and KC13 was the oldest).

Figure 11

Figure 6. Scatter plot of mean VOTs against Kriol-speaking children’s age (in months).

Figure 12

Figure 7. Individual mean constriction durations (CD) of voiced and voiceless stops in medial position. The numbers above each child’s mean values in the figure indicate the difference in constriction duration between the voiceless and the voiced stops. Children are coded based on their age (i.e., KC01 was the youngest and KC13 was the oldest).

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Figure 8. Acceptance rates in the mispronunciation detection task. (A) Box plot of acceptance rates by different phonological modifications. (B) Individual acceptance rates by Kriol-speaking children’s age.

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Table 7. Summary of fitted generalised linear mixed-effects models (GLMMs) for Kriol-speaking children child. Post-alveolars are excluded due to limited observations

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Table 8. Post hoc tests between modification types

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Appendix 1a: Number of tokens per child for word-initial consonants

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Appendix 1b: Number of tokens per child for word-medial consonants

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Appendix 2. Full list of target words for the mispronunciation detection task. Column one presents the target words in IPA, numbered in accordance with the presentation order. Column two indicates the correct pronunciation of the target word. Column three provides English gloss, and Column four lists the type of mispronunciation in each mispronounced target. Where segments have been modified (75% of the targets), the relevant segment is bold in the IPA target word