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Pop Song English as a supralocal norm

Published online by Cambridge University Press:  11 April 2023

Andy Gibson*
Affiliation:
Macquarie University, Australia
*
Address for correspondence: Andy Gibson Centre for Language Sciences Macquarie University, Sydney 16 University Avenue, Macquarie University NSW 2109, Australia gibsonism@gmail.com
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Abstract

An American-influenced singing accent, referred to here as Pop Song English (PSE), is common in popular music throughout (and beyond) the Anglophone world. This article presents an analysis of the sung pronunciation of two variables (bath and nonprevocalic /r/) that distinguish New Zealand English (NZE) from American Englishes (AmE). The Phonetics of Popular Song (PoPS) corpus includes 154 performers, structured according to country of origin (NZ and the US) and musical genre (pop and hip hop). An auditory analysis was conducted for each variable, distinguishing between the NZE and PSE/AmE variants. Almost all New Zealand performers adopt the PSE variants at least some of the time, with greater adherence to the American model in pop than in hip hop. In the US, region determines hip hop, but not pop, artists’ degree of rhoticity. PSE represents a supralocal norm for pop music, while hip hop artists tend to use their ‘own accent’. (Pop Song English, singing accent, rap accent, supralocal norm, nonprevocalic /r/, trap–bath split, intentionality, language performance, pop music, hip hop, responsive style, initiative style)*

Information

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BY
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
Copyright © The Author(s), 2023. Published by Cambridge University Press
Figure 0

Figure 1. Mean F1 and F2 of sung (n = 116) and spoken (n = 161) vowels for Dylan Storey, reproduced from Gibson (2010). Labels for diphthongs at arrow heads.

Figure 1

Table 1. Number of songs in each cell of the PoPS corpus, with number of unique artists in brackets.

Figure 2

Table 2. Mean rate of realisation of bath words with trap (/æ/) for each combination of genre and country, with token counts. Means of by-performer means are also given since token counts vary between performers.

Figure 3

Figure 2. bath model (n = 301): Predicted probability of realising bath with trap (/æ/) according to genre and country of artist. Lines connect the predictions from the model fit for each genre category, back-transformed to probabilities. Small points (plotted with jitter for readability) show each individual performer's mean rate of trap.

Figure 4

Table 3. Mean % /r/ realisation and token counts for rhoticity data, grouped first according to genre and country, and then according to whether the potential /r/ occurs in a nurse environment or not. Means of by-performer means are also given since token counts vary between performers.

Figure 5

Figure 3. Rhoticity model for all data (n = 3242): Predictions from interaction of genre and country (larger points connected by lines) plotted with individual performers’ proportion of /r/-presence (small points).

Figure 6

Table 4. Mean % /r/ realisation and token counts for rhoticity data from US artists only, grouped according to genre and the performer's region of origin. Means of by-performer means are also given since token counts vary between performers.

Figure 7

Figure 4. Rhoticity model for US data only (n = 1360): Interaction of genre with region (lines) plotted with individual performers’ proportions of /r/-presence (points).