The MEAT-MATE merger The MEAT-MATE The apparent merger of ME / / - PDF document

The MEAT-MATE ‘merger’ The MEAT-MATE • The apparent merger of ME / ɛː / (MEAT) and /a ː / (MATE) ‘merger’ in Mid-Ulster English – in Early Modern English – in traditional English dialects, e.g. varieties of Irish English revisited – by Jaysus , tay , Juno and the Paycock • With subsequent ‘reversal of merger’, without hypercorrection Warren Maguire – which is meant to be essentially impossible (Labov 1994) – are/were the two vowels actually merged or were they in a w.maguire@ed.ac.uk situation of near merger? The MEAT-MATE ‘merger’ in The apparent reversal of the Mid-Ulster English MEAT-MATE merger in MUE • MATE (= FACE) – /e/, with two well known allophones, [ ɪə ] (default) and [e ̞ː ] (in MEET MEAT MATE morpheme final position); daze ≠ days (Wells 1982: 440-1) – [e ̞ː ] in morpheme final position has even been interpreted as an a ː ME allophone of an entirely different phoneme, / ɛ / (ibid.) eː ɛː – third allophone before palato-alveolars and velars, [e ˑ ]/[ ɪˑ ] ( bake , Traditional nation ) i ‘e’ e MUE • MEAT Transitional i ‘e’ ~ i e MUE – with /i/: non-traditional, standard, now general Standard i i e – with an /e/-type vowel, seemingly the same as MATE MUE – ‘/e/’ in MEAT is traditional, non-standard, now stigmatised and deeply buried in the vernacular Milroy and Harris (1980), Harris (1985) M&H’s analysis • Auditory analysis of MEAT and MATE in conversational Belfast Vernacular English (BVE) • MATE and /e/-type MEAT only; /i/-MEAT excluded – were the two sets in a state of merger or near merger? • Informants and group scores • MATE-like pronunciations of MEAT are very deeply – data from 8 male speakers, data analysed at the group level buried in the most informal vernacular – 60 ‘/e/’ MEAT tokens (about 1 per hour!), 99 MATE tokens – in read speech, speakers invariably produce /i/ in MEAT – i.e. only 7.5 ‘/e/’ MEAT tokens per speaker on average • When asked to produce their ‘broad’ MEAT • Auditory analysis pronunciations, speakers found this to be an artificial – data quality poor (multiple speakers, background noise, etc.) exercise, and M&H (1980: 202) did not trust the results, – determining nucleus height and presence/absence of off-glide which appeared to show merger – environments: -t, -l, -n, -g and following voiced fricatives – cf. Labov (1994: 359) “Speakers who make a consistent – i.e. no analysis of how the allophonic conditioning interacts with difference in spontaneous speech often reduce this difference in the ‘merger’ more monitored styles.” 1

M&H’s results M&H’s conclusions MATE MEAT Height Glide No glide Glide No glide Height Vowel MATE MEAT 1 33 0 0 0 • The two sets are not the same, they are in a situation of 1 33 0 2 54 6 18 2 near merger (see Labov 1994: 349-370) ɪə 2 60 20 3 4 2 18 20 e, eə – “they overlap to a certain extent. It is not simply that they 3 e ̞, e̞ə 6 38 4 0 0 0 2 approximate closely in phonetic space: realizations of them are 4 0 2 91 8 36 24 sometimes the same.” (M&H, p. 206) ɛ – this variable identity means that they can easily be rhymed and spelt the same • MEAT significantly lower than MATE; typically [e̞], [e̞ə] or [eə] • This explains how the apparent MEAT-MATE ‘merger’ in MUE has been reversed • MATE is significantly more likely to have a centring off- – it never was a merger in the first place; rather, it was a near glide than MEAT; typically [ɪə] or [eə], sometimes [e] merger – partly because off-glides are more common with higher nuclei – M&H (and Labov) suggest that the same kind of situation must have pertained in Early Modern Standard English New data needed Questions • Given these questions, it is desirable to have: • Are 60 MEAT tokens (av. 7.5 per speaker) enough to say anything valid (especially given the different environments)? – more data per speaker – good quality data for acoustic analysis • Since merger/distinction is really a property of individual – more detailed consideration of phonological conditioning, phonologies, are group scores meaningful? including duration – targeted elicitation and speaker judgements? • Is an auditory analysis of poor quality recordings sufficient for identifying a near merger? How accurate can it be? • We need a variety where the apparent MEAT-MATE merger is still relatively common • How does the near merger interact with the significant – it just so happens I know exactly allophonic variation (e.g. TEA-DAY and BEAK-BAKE)? such a variety… – Tyrone English (Fintona area) • Is it the case that this feature can’t be assessed using – TyrE formal elicitation techniques? My intuitions Examples • I am a native speaker of TyrE, with variation between /i/ and ‘/e/’ in MEAT • KF (F, 1919): Mind you, life wasn’t too easy, but it was all right. I said - - life was a happy one but it was a hard one. • I think there might be a distinction before voiceless fricatives and /k/ at least (environments not tested by • WB (M, 1975): And I left it and I was clean beat, so I done it in his class. M&H) RM (M, 1943): Then they’re took in, showed in a seat into the room – consisting of a minor durational difference, with more • if there’s - sometimes there doesn’t be standing room. You’d be diphthongisation in longer phones when not before /k/ sitting every-road, you know, and even on a good night in some – peace ≠ pace, sheaf ≠ safe, beak ≠ bake houses there if they weren’t too big a house(s) they’d be sitting – I can’t detect any definite difference before morpheme outside. And everybody gets tea and a sandwich and maybe biscuits boundaries nor before other consonants: tea = Tay, beat = bait and stuff passed round and if it’s a Catholic wake they normally pass round cigarettes and sometimes bottles of stout. And, uh, then, uh, • Labov (1994: 359) on near merger: whenever, before you leave you’re took in always to see the dead person, you see. – “Phoneticians from other areas are better able to hear the difference than the native speakers.” 2

Minimal pair tests The TyrE corpus • Minimal pair and rhyme tests were conducted with two • Ongoing collection of corpus of local speech speakers, RM (M, lots of ‘/e/’ in MEAT in everyday – Interviews with 11 speakers from countryside around Fintona conversation) and JK (F, almost no ‘/e/’ in MEAT) (each 40 mins +) – 4 female, 7 male; all but one born 1954 or earlier • Both speakers were well aware of the possible broad – 10 Protestant, 1 Catholic; all rural working-class pronunciations of MEAT and were asked to compare – Supplemented by a small collection of recordings of older these to their pronunciations of MATE members of the community (mostly now dead) made in the late 1980s • Neither speaker felt there were any differences between MEAT and MATE in a range of environments • Lots of data for MATE-like pronunciations of MEAT, but still not as much as I’d like – JK: beak-bake peace-pace – RM: beat-bait reason-raisin – 200 ‘/e/’ tokens of MEAT in total Initial analysis of MEAT WB and RM Speaker /e/-type /i/ • WB: M, b. 1975, farmer JK (F, 1950) 1 (5.3%) 18 – 1 -# token, with /i/ EB (F, 1949) 1 (4.3%) 22 – 1 -tʃ and 3 -k tokens, all with ‘/e/’ MM (F, 1940) 8 (9.6%) 75 – 39 other MEAT tokens, 33 with ‘/e/’, 6 with /i/ KF (F, 1919) 4 (33.3%) 8 WB (M, 1975) 37 (84.1%) 7 • RM: M, b. 1943, farmer JW (M, 1954) 0 (0.0%) 2 – 23 -# tokens, 14 with ‘/e/’ (all tea ), 9 with /i/ ( peacock and sea ) VM (M, 1945) 15 (25.9%) 43 – 2 -tʃ tokens with /i/, 1 -k token with /i/, 2 -k tokens with ‘/e/’ RM (M, 1943) 68 (73.9%) 24 – 64 other MEAT tokens, 12 with /i/, 52 with ‘/e/’ SC (M, 1939, Cath.) 21 (75.0% 7 DE (M, 1938) 15 (60.0%) 10 • F1 and F2 at 20%, 50% and 80% compared with similar KM (M, 1926) 13 (86.7%) 2 samples of MATE; duration not analysed RK, RG, SM (M, 1900-1925) 17 (85.0%) 3 WB RM F2 (Hz) F2 (Hz) F2 (Hz) F2 (Hz) 2100 1900 1700 1500 1300 2100 1900 1700 1500 1300 2400 2200 2000 1800 1600 1400 2400 2200 2000 1800 1600 1400 300 300 300 300 400 400 400 400 MEAT 20% MEAT 50% MEAT 20% MEAT 50% MATE 20% MATE 50% MATE 20% MATE 50% 500 Av. MEAT 20% 500 Av. MEAT 50% 500 Av. MEAT 20% 500 Av. MEAT 50% Av. MATE 20% Av. MATE 50% Av MATE 20% Av. MATE 50% 600 F1 (Hz) 600 F1 (Hz) 600 F1 (Hz) 600 F1 (Hz) F1: p = 0.135 F1: p = 0.016 F1: p = 0.014 F1: p = 0.396 F2: p = 0.002 F2: p = 0.001 F2: p = 0.300 F2: p = 0.759 700 700 700 700 F2 (Hz) F2 (Hz) F2 (Hz) F2 (Hz) 2100 1900 1700 1500 1300 1800 1700 1600 1500 1400 2400 2200 2000 1800 1600 1400 2300 2200 2100 2000 1900 1800 300 450 300 400 400 400 450 MEAT 80% MEAT 80% MEAT MATE 80% MEAT MATE 80% MATE 500 Av. MEAT 80% 500 500 500 MATE Av. MEAT 80% TEA Av. MATE 80% Av. MATE 80% DAY F1 (Hz) F1 (Hz) 600 600 F1 (Hz) 550 F1 (Hz) F1: p = 0.582 F1: p = 0.540 F2: p = 0.030 F2: p = 0.385 700 550 700 600 3