ENG 528: Language Change
Research Seminar
Sociophonetics: An Introduction
Chapter 5: Vowels
Duration
Several uses in speech:
• phonological contrasts in quantity (i.e., long vs. short
vowels, tense vs. lax vowels, single vs. geminate
consonants)
• as a phonetic cue for other phonological distinctions
(especially voiced vs. voiceless consonants, but also
fricatives vs. stops, etc.)
• word stress
• other prosodic functions, especially phrase-final
lengthening
• overall rate of speech
Determining onsets and offsets
Cues to watch for:
• vocal pulsing
• appearance or cessation of F2
• aspiration or frication
• stop bursts
• basically, any discontinuity
• adjacent vowels and contiguous vowels and
approximants present special problems
Some Examples
Know what formant patterns to
expect!
200
i

y
300
u


e

400
o

500
F1 in Hz
•F1 and F2 are far apart
for high front vowels
•F1 and F2 are close
together for low back
and back rounded
vowels
•F1 is inversely
correlated with height
•F2 is directly correlated
with advancement
•Lip rounding lowers
some formant values


600




700

800
900
a
1000
2500
2000
1500
F2 in Hz
1000
500
Where do you measure?
It depends on what you want to show:
• Are you looking at vowel shifting patterns?
• Are you showing means or individual tokens?
• Are you interested in dynamic patterns of
vowels?
• If so, are you looking at general
diphthongization, consonantal transitions, or
details of formant trajectories?
If you need only one point
• for studies of vowel shifting
• for comparison of different tokens of a
phoneme to check for conditioned allophony
• for comparison of different tokens to see how
much spread there is or if the spread shows a
geometric pattern
• to see how much different phonemes overlap
(especially if a merger is possible)
Where to measure a single point
• Dead center of the vowel: less subjective than other
methods, but
 no good for diphthongs
 doesn’t always represent a vowel’s closest approach to
its target
• Points where F1 or F2 reach extreme values:
 usually works, but
 problematic if consonantal transition patterns cause the
onset or offset to show the most extreme value
 extreme F1 and F2 patterns don’t always match up
• In a steady state
 if there is a steady state, that is
If you need more than one point
• Used to examine dynamic aspects of vowels
• Dynamic aspects include:
 Diphthongization: usually, you need only two or
three points
 Transitions to and from neighboring segments; only
two or three points are needed
 Sometimes, more local patterns, such as
convex/concave patterns, interference from
harmonics, etc.; you need a lot of points for these
kinds of problems
• Be aware that too much data on one graph becomes
hard to read
Where to measure multiple points
Two basic approaches:
• At even intervals through the vowel
(percentages or fractions)
• At specified distances in ms from each other
or from onset or offset
• Each has advantages and disadvantages
An example
• word is cloud
Plotting (1)
• Old-fashioned way: from Labov, Yaeger, & Steiner
(1972)
• Individual tokens with ellipses
Plotting (2)
• A plot from Labov, Ash, & Boberg (2006)
• Individual tokens, but no ellipses; utilizes Plotnik
Plotting (3)
BOT early
BOT midpoint
BOT late
BOUGHT early
BOUGHT midpoint
BOUGHT late
500
300
law
600
400
got
500
700
600
F1
F1
Individual tokens
are best for:
examining
mergers
testing for
vowel
dynamics
looking at
internal
configuration
of a phoneme
800
700
900
800
not
1000
900
1000
2000 1800 1600 1400 1200 1000 800
F2
1100
2200 2000 1800 1600 1400 1200 1000
F2
Plotting (4)
• Mean values—my favorite method
400
i
500
T
r
i
K
l=ul
u
u
<tour>
r
600
e
e
r
'
o

N

N


N
æ
F1
700
800
ai
au
d
æ
3000
F
2500

 
v
o
l
=
=







ai
=


g
æ
1000
oi

o
900
r
r
=o

æ
P
æ
2000
1500
F2
1000
500
Plotting (4)
• You can try showing standard deviations
300
i
400
r
i
<school>
.
.
e
u


e
500
r

600



F1
æ ..
T
æ
700
F
æ
800
o
r
'
D
o
o
r
v . . au
ai
o
ai
oi




900
1000
2500
2000
1500
F2
1000
l
Plotting (5)
• Trajectories are mainly used to examine vowel
dynamics
350
GOOSE
FLEECE
400
GOAT
FACE
450
KIT
NURSE
NEAR
F1
500
SQUARE
550
CHOICE
PRICE
DRESS
600
THOUGHT
=NORTH
=FORCE
STRUT
=FOOT
START
LOT
PRIZE MOUTH
650
TRAP=BATH
700
2200
2000
1800
1600
1400
F2
1200
1000
800
Vowel Normalization (1)
Aims of normalization
1. Eliminate variation due to physiological
differences
2. Preserve lectal and linguistic differences
3. Keep contrastive vowels separate
4. Reflect how auditory normalization works
Different scholars have different aims, but they
don’t always understand that
Vowel Normalization (2)
•
•
•
A procedure some sociolinguists use to get around vowel normalization is
comparison of two vowels
E.g., for the Southern Shift, compare whether /e/ or / / has a higher nucleus
Labov, Ash, and Boberg (2006) made extensive use of vowel comparison to define
the Northern Cities Shift area
Vowel Normalization (3)
• Lots of mathematical techniques have been
developed to perform normalization
• One important fact to keep in mind: There’s
no such thing as a perfect normalization
technique!
• We’ll combine section 5.6 in the book with
Clopper (2009) in what follows
Vowel Normalization (4)
• One way to divide normalization: vowelintrinsic vs. vowel-extrinsic
• Vowel-intrinsic: each vowel is normalized on
its own—all information is taken from that
vowel
• Vowel-extrinsic: vowels are normalized
relative to each other—information is taken
from multiple vowels
Vowel Normalization (5)
• Another division: scale-factor vs. range
normalization
• Scale-factor: a single scale factor is utilized
• Range: the range of formant values that the
speaker exhibits is involved
Vowel Normalization (6)
• One more division: speaker-intrinsic vs.
speaker-extrinsic
• Speaker-intrinsic: each speaker is normalized
on their own—all information is taken from
that speaker. Most methods do this.
• Speakers are normalized relative to each
other—information is taken from multiple
speakers. Labov et al. (2006) did this.
Vowel Normalization (7)
• Vowel-intrinsic scale-factor: Bladon et al.
• Subtract 1 Bark from all female formants
• Problem with F1
Vowel Normalization (8)
• Vowel-intrinsic range: Syrdal & Gopal
• Z1-Z0 and Z3-Z2 (Bark-converted)
• Better, but still some trouble with the height
dimension
Vowel Normalization (9)
• My modification of Syrdal & Gopal
• To avoid F0-related problems, use Z3-Z1 and Z3-Z2
• Still some height distortion
FLEECE
11
NEAR
FLEE
CE
12
central Ohio female
central Ohio male
TOOT
PRICE
PIN=PEN
Z3-Z1 in Bark
GOA
T
HAN
D
PIN=PEN
FACE
GOOSE
POOL=PULL
KIT
TOOT
NEAR
OT
CHOICE
FO
10
FA
CE KIT E
POLE
R E
STRUT
GOOSE
UAPRIC SS
Q
S
DRE
HAND
FOOT
T
POOL=PULL
GOAT
STRU CHOICE
SQUARE
9
NORTH=FORCE
DRESS
NURSE
NORTH=FORCE
POLE
MOUTH
LOT=T
HOUGH
T
LOT=T
TRAP=BATH
HOUGH
8
NURSE
T
TRAP=BATH
T
AR
ST UTH
MO E
IZ
PR ZE
I
PR
START
7
1
2
3
4
5
Z3-Z2 in Bark
6
7
8
Vowel Normalization (10)
• Vowel-extrinsic scale-factor: Nearey, Watt & Fabricius
• For Nearey, F*n[V] = anti-log(log(Fn[V]) - MEANlog), where F*n[V]
is the normalized value for Fn[V], formant n of vowel V, and
MEANlog is the log-mean of all F1s and F2s for the speaker
• Watt & Fabricius compute a single scale for both F1 and F2
Vowel Normalization (11)
• Vowel-intrinsic range: Lobanov
• Fn[V]N = (Fn[V] - MEANn)/Sn, where Fn[V]N is the
normalized value for Fn[V] (i.e., for formant n of vowel
V); MEANn is the mean value for formant n for the
speaker and Sn is the standard deviation
Vowel Normalization (12)
• Achilles heel of vowel-extrinsic techniques:
they’re thrown off when used to compare
very different vowel inventories
0.4
FLEECE
FLEECE
OT
TO
TOOTGOOSE GOOSE
0.5
KIT
FACE
NEAR
E
CUR
POOL
NURSE
SQUARE
FORCE
GOAT
FOOT
POOL=
PULL
NORTH=
FORCE
GOA
CHOICE
T
HAND
NORTH
SQUARE=BERTH
H
T
T
U
DRESS
POLE
MO STRU
PRICE
STRUT L NUR
OT SE
PICK
=
CH THO
MOUTH
U
O
E
ICE GH
STA
ICIZE
T
R
DRESS
RT
P PR KIT
TRAP=BATH
LOT
=TH
OUG
BACK
FIVE
HT
FACE
0.6
T
AR
ST
Normalized F1
Northern Ireland male
central Ohio male
NEAR
0.7
0.8
TRAP=BATH
2.8
2.6
2.4
2.2
2.0
1.8
1.6
Normalized F2
1.4
1.2
1.0
0.8
Vowel Quality/Voice Quality
Interaction
• We’ll save this for when we get to chapter 7
Steady-State Patterns (1)
• For a fully realized diphthong, besides the
transitions at the onset and offset, you can have:
 A nuclear steady state
 A transition between the nucleus and glide
steady states
 A glide steady state
• Not all diphthongs have both steady states
• The steady states can also vary in duration
Steady-State Patterns (2)
• aid and day
Steady-State Patterns (3)
• Quantifying steady states is a problem
• You can look at degree of change in formant
values
• There are probably statistical procedures for
this sort of thing
• Steady states can be used for perception
experiments: see goodness experiments in
Peeters (1991)
Undershoot
• This will be next week’s topic
References
•
•
•
•
•
•
•
•
•
Diagrams on slides 13 and 19 are taken from:
Labov, William, Sharon Ash, and Charles Boberg. 2006. The Atlas of North
American English: Phonetics, Phonology and Sound Change. A Multimedia
Reference Tool. Berlin: Mouton de Gruyter.
Diagrams on slide 14 are taken from:
Thomas, Erik R. Forthcoming. Sociophonetics. The Handbook of Language
Variation and Change. Ed. J. K. Chambers and Natalie Schilling-Estes. 2nd edn.
Oxford, UK/ Malden, MA: Wiley-Blackwell.
Diagrams on slides 24, 25, 27, & 28 are taken from:
Clopper, Cynthia G. 2009. Computational methods for normalizing acoustic vowel
data for talker differences. Language and Linguistics Compass 3:1430-42.
Other references:
Labov, William, Malcah Yaeger, and Richard Steiner. 1972. A Quantitative Study of
Sound Change in Progress. Philadelphia: U.S. Regional Survey.
Peeters, Wilhelmus Johannes Maria. 1991. Diphthong dynamics: A cross-linguistic
perceptual analysis of temporal patterns in Dutch, English, and German. Ph.D.
dissertation, Rijksuniversiteit te Utrecht.