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Comparative Acoustic Analyses of L2 English: The Search for Systematic Variation

  • Rebecca Laturnus
From the journal Phonetica

Abstract

Background/Aims: Previous research has shown that exposure to multiple foreign accents facilitates adaptation to an untrained novel accent. One explanation is that L2 speech varies systematically such that there are commonalities in the productions of nonnative speakers, regardless of their language background. Methods: A systematic acoustic comparison was conducted between 3 native English speakers and 6 nonnative accents. Voice onset time, unstressed vowel duration, and formant values of stressed and unstressed vowels were analyzed, comparing each nonnative accent to the native English talkers. A subsequent perception experiment tests what effect training on regionally accented voices has on the participant’s comprehension of nonnative accented speech to investigate the importance of within-speaker variation on attunement and generalization. Results: Data for each measure show substantial variability across speakers, reflecting phonetic transfer from individual L1s, as well as substantial inconsistency and variability in pronunciation, rather than commonalities in their productions. Training on native English varieties did not improve participants’ accuracy in understanding nonnative speech. Conclusion: These findings are more consistent with a hypothesis of accent attune­ment wherein listeners track general patterns of nonnative speech rather than relying on overlapping acoustic signals between speakers.


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*Rebecca Laturnus, Department of Linguistics, New York University, 10 Washington Place, New York, NY 10003 (USA), laturnus@nyu.edu

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  1. 1

    We analyze the productions of only one speaker of each accent here because they are the sole voices listeners were exposed to during the perceptual experiment reported in Laturnus (2018). The goal is to evaluate the explanatory power of the hypotheses described in section 1 to account for the attunement and generalization effects observed in the literature. If the proposed hypotheses are adequate to explain these effects, acoustic analysis of only the experimental stimuli themselves should be required.

  2. 2

    Due to ethics restrictions on the NUFAESD corpus, it was not possible to gain access to the exact speakers’ recordings used in Baese-Berk et al. (2013). The Wildcat Corpus was used instead as a close approximation.

  3. 3

    The Italian and Thai speakers’ ratings are more than 1 point above the other speakers; however, it was decided these language backgrounds should be included so as to approximate those in the study of Baese-Berk et al. (2013) as much as possible. Spanish is the only other romance language in the Wildcat Corpus, and that speaker’s rating is also 7.0.

  4. 4

    Because VOT measurements had a skewed distribution, log-transformed values were used in the models (following, e.g., Baese-Berk & Goldrick, 2009, Fricke, 2013, Stuart-Smith et al., 2015, Piccinini & Arvaniti, 2015).

  5. 5

    Major metropolitan cities are defined here as populations over 100,000 people, as this corresponds the most clearly to having more than 25% of the city’s population being nonnative speakers of English, according to 2011 census data (Gambino et al., 2014).

  6. 6

    30% was chosen as the criterion in these questions to correspond to the criteria used in recruitment, where census data indicate metropolitan areas over 100,000 people have a substantial portion of the population being nonnative English speakers (see footnote 4).

  7. 7

    The IAT is a reaction-time experiment. A D-score is calculated on test trials by dividing the difference in mean reaction time between trial types by the overall standard deviation.

Received: 2017-08-15
Accepted: 2020-05-01
Published Online: 2020-07-21
Published in Print: 2020-12-01

© 2020 S. Karger AG, Basel

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