Dear Sarah, dear all,
One of the misconceptions about the Watt and Fabricius method that tends to
float around (and you are by no means alone on this, Sarah!) is the idea
that the point corner vowels to be used HAVE to be beet, bat and the u'
construct. This is not what we wrote in the original article. What we wrote
was that each speaker's vowel space should be normalised using a) the vowel
which has the highest F2 and lowest F1, i.e. at the top left corner of the
vowel space; b) the vowel that has the highest F1, wherever in the
back/front dimension it is - the modified W&F method (which is proving to be
the better performing one of the two in some studies) leaves out the F2
value of this vowel, so that it does in effect find the lower centre point
of the vowel space, and c) the u' value which is constructed using the F1
of the top left corner, and F1=F2.
For any individual spaker, then, it has to be determined which measurable
vowels in the high front vowel space and the low vowel space best correspond
to the descriptions in a) and b9 above. It will not necessarily be the same
vowel categories that will fulfill these conditions, *especially if you are
dealing with regionally-varied origins. You don't specify where your data
come from, but I would suggest that looking at each speaker from this
perspective would be a place to start the detective work.
Another important issue to consider is that the choice of normalisation
algorithm method should be based on the method NOT removing the data details
that we hear and want to see documented in measurable data. Testing
explicitly just exactly what any normalisation method does to the data we
give it is an important methodological step, and we need to document this
and understand how we made that decision. In other words, if, after you have
followed the steps I suggest above, the problem remains, then the next step
is to go to a closely-related method such as Lobanov or the
formant-intrinsic, vowel-extrinsic version of Nearey, and see how the three
methods perform with your data. On that basis you will have a solid
foundation to be able to argue "I chose X, because I tested X, Y and Z and X
did the optimum job according to the criteria I had in the study, ie that I
wanted to be able to see features a b c in the data after normalisation".
Hope this helps!
Anne
_____________________________________________________
Dr. Anne H. Fabricius
Associate Professor of English Language
Director of Studies, English Programme, Department of Culture and Identity
Member of CALPIU Research Centre
www.calpiu.dk
Bldg 3.2.5
Roskilde University
DK-4000 Denmark
__________________________________________________
-----Original Message-----
From: VAR-L automatic digest system
Sent: Friday, April 29, 2011 1:03 AM
To: [log in to unmask]
Subject: VAR-L Digest - 20 Apr 2011 to 28 Apr 2011 (#2011-41)
There is 1 message totaling 47 lines in this issue.
Topics of the day:
1. Normalisation of vowels
----------------------------------------------------------------------
Date: Thu, 28 Apr 2011 23:41:32 +0100
From: Sarah Haigh <[log in to unmask]>
Subject: Normalisation of vowels
Hi everyone,
I was wondering if someone with more experience of vowel normalisation could
possibly share it with me as I'm facing something of a dilemma. To try to
summarise, I'm working on three vowels (GOAT, NORTH and PRICE) and have been
analysing them individually. I have 16 male speakers, which I am analysing
in four regionally-based groups using the Watt & Fabricius method on the
NORM suite website. When considering monophthong variants in particular,
GOAT and PRICE seemed to give me results that were broadly in line with what
I was expecting, but the results for NORTH did not correspond at all with
what I could hear through plain auditory analysis.
I subsequently realised I'd made a mistake and hadn't included the "corners"
of the vowel spectrum on the NORM form - ie, the 'bat', 'beet' and 'school'
values. However, having gone back and included these, the results look very
much the same as they did without! I'm not discounting the possibility that
I'm making some other error, or even that normalisation isn't appropriate
for the data I'm trying to put into it. So if anyone could give me any
thoughts I'd be very grateful! And if I *am* doing things right (or even if
I'm not), why should GOAT and PRICE give me the results I predicted, while
NORTH does not seem to at all?
Any thoughts appreciated!
Many thanks,
Sarah
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