Methods

Materials

We analysed 35 Christmas broadcasts of Queen Elizabeth II (recorded in 1952, 1954-1961, 1963-1966, 1968, 1970-1973, 1983, 1985, 1988, and 1994, 1996-2002, 2008, 2010, 2014, 2015, 2016, and 2017). Their mean duration was 5 minutes with a range between 59 seconds in 1959 and just over 8 minutes in 1956. These recordings span the age range of 26-91 years. We selected and analysed target vowels that (a) formed the

Table 5.1 Number of occurrences of each vowel type selected as either an anchor (left) or target (right) vowel

Anchor vowels

Target vowels

FLEECE

/i:/

DRESS

/с/

PALM

Ы

THOUGHT

Ы

happY

IrJ

TRAP

/ж/

GOOSE

who’d

Ы

HEWED

/ju:/

1950s

229

462

115

217

153

66

38

24

1960s

173

360

59

133

120

20

25

24

1970s

106

223

34

90

86

14

14

27

1980s

102

244

43

100

55

47

19

26

1990s

198

314

96

152

128

36

30

29

2000s

121

224

44

106

74

14

22

17

2010s

166

269

70

147

118

57

29

28

I( = 5928)

1095

2096

452

945

734

254

177

175

352

nuclei of stressed syllables of nuclear accented words in the lexical sets trap (/se/) and goose (/u:/) and (b) vowels in lexically unstressed syllables of words in the lexical set happY (annotated as Iv.l, as in Harrington 2006). Additionally, we selected (c) so-called anchor vowels (see below) in lexically stressed syllables from the lexical sets fleece (/i:/), dress (/e/), palm (/a:/), and thought (h:/). Altogether, 5928 vowels were selected (of which 1340 were target vowels); their distribution by decade is shown in Table 5.1.

Following our own earlier practice (e.g., Harrington et al. 2000b), we separated words of the lexical set goose depending on the absence (e.g., who’d, cooed) or presence (e.g., hewed, queued) of a preceding ll context. In the latter cases, the segment of interest then was the entire /ju:/ sequence.

Data Preparation and Analysis

Using the transcripts of the Christmas broadcasts (which can be downloaded from https://www.royal.uk/history-christmas-broadcast?ch=5# bio-section-4), we applied grapheme-to-phoneme conversion and forced-alignment (within the Munich Automatic Segmentation System for British English, Kisler et al. 2017) for the automatic segmentation and labelling of the audio recordings. Any necessary manual readjustment of the target and anchor vowels were carried out in the EMU- Speech Database Management System (Winkelmann et al. 2017).

The frequencies of the first five formants (F1-F5) were calculated from the audio signals (sampling rate 16 kHz) in the frequency range 0 to 5500 Hz with a frame shift of 6.25 ms and a window length of 25 ms by means of PRAATs (Boersma and Weenink 2018) built-in standard formant tracker using the Burg method (cf. Childers 1978: 252-255). Obvious errors in the first two formants of the target vowels, such as when FI was mistracked as F2, were manually corrected.

The linearly time-normalized (to 11 points) FI and F2 trajectories were smoothed using a 5-point median filter. In order to preserve the dynamic information, the discrete cosine transformation (DCT) was applied to these time-normalized trajectories of FI and F2 (Watson and Harrington 1999). For this purpose, we extracted the first three DCT coefficients, k0, kj, k2, which are proportional to the mean, the linear slope, and the curvature and thereby encode the salient aspects of a vowel’s dynamics with just three values per formant (Watson and Harrington 1999).

We tested our hypotheses by calculating the relative distance between the target vowel and two anchor vowels (see Table 5.2) in a space formed from the DCT-transformation of either one or both formants. This method of relative distances was used in order to normalize for possible changes to vowels’ formants that are a consequence of

Table 5.2 The target vowel, anchor vowels and the DCT-space in which the relative position of the target vowel between the anchors was calculated

Target vowel

Anchor vowel 1

Anchor vowel 2

Space (dimensions)

TRAP

FLEECE

PALM

DCT-F1 (= 3D)

GOOSE

FLEECE

THOUGHT

DCT-F2 (= 3D)

happY

FLEECE

DRESS

DCT-F1 and F2 (= 6D)

(physiologically induced) age-related changes to the vocal tract (cf. Reubold and Harrington 2015, 2018). More specifically, since the sound change to trap involves changes in phonetic height, the position of trap was calculated in relation to the phonetically high fleece and phonetically low palm vowels in an Fl-space (which indexes phonetic height). Since the sound change to goose involves /u/-fronting, its position was calculated in an F2-space relative to the phonetically front and back vowels respectively fleece and thoucftt. Finally, given that tensing of the final vowel in happY involves both fronting and raising, its position was calculated in relation to the high front fleece and phonetically lower and more central dress vowels in a combined FI and F2 space. In all cases, the spaces were created from the DCT-coefficients (thus e.g., a three-dimensional DCT-F1 space in the case of trap - see Table 5.2).

This relative distance of the target vowel to the two anchor vowels was calculated from op, the orthogonal projection ratio (Stevens et al. 2019) using (1):

in which in a DCT-space (formed from the DCT-coefficients of FI, F2 or both formants, see Table 5.2), Tc is the position of a given target vowel, Т?л and ~cai are the centroids (means) of the two anchors respectively, and О is the scalar (inner) product of two vectors. When op = 0, then the target is equidistant between the two anchors and when op = +1 or -1, then the target is positioned at one of the two anchors respectively. If op is outside these ranges, then the target is beyond either anchor (see Stevens et al. 2019, for further details).

As in Reubold and Harrington (2018), we wanted to test for possible effects of lexical frequency by using so-called Zipf values (Van Heuven et al. 2014), i.e., standardized measures of word frequencies, taken from a database that is based on subtitles of British TV programs (SUBTLEX- UK, cf. Van Heuven et al. 2014), thereby aiming at representative frequency measures of spoken language (as opposed to written language resources). The Zipf value of a given word, Zipfword, could vary between 0 and 9 and was calculated from (2):

in which n is the number occurrences of a particular word in the SUBTLEX-UK database.

Elizabeth II often uses quite frequent words in the Christmas broadcasts: the mean of Zipfword in the selected target words was 5.07 (median = 5.25, SD = 1.01, range: 1.81 to 7.32). Usually, the cut-off between “infrequent” and “frequent” words is chosen at Zipfword > 4 (Van Heuven et al. 2014). Since, however, only 201/1340 of the selected words were labelled “infrequent” by this measure, we instead selected a cut-off of Zipfword > 5 resulting in 755 frequent and 585 less frequent words.

We ran three separate mixed models (one for each of the happY, trap, and goose targets) in the R package ImerTest, version 3.1-0 (Kuznetsova et al. 2017), which makes use of the techniques in the package lme4, version 1.1-21 (Bates et al. 2015). For plotting the fixed effects (see Figure 5.2), we used the package effects, version 4.1-0 (Fox and Weisberg 2018). The dependent variable was always op in (1) and the independent variables were Age (in years) and the quadratic term Age2 as a numerical regressor and a binary fixed factor Lexical Frequency (with levels frequent and less frequent). The quadratic term Age1 was included in order to test for a retrograde change, i.e., to test whether the change was initially towards, and then in late adulthood against, the direction of community change. The word types were the levels of the random factor Word. In order to obtain p-values, Satterthwaite’s method was used to calculate an approximation to the effective degrees of freedom.

 
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