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Running title: Sonority-based segmentation 1
Sonority effects on English word segmentation:
Evidence for syllable-based strategies in a stress-timed language
Jason B. Bishop
College of Staten Island and The Graduate Center,
City University of New York
Suggested Running title: Sonority-based segmentation
Word Count: 2,991 words
Correspondence address: Jason B. Bishop Department of English College of Staten Island, City University of New York 2800 Victory Blvd. Staten Island, NY 10314 Telephone: (718) 982-3665 Fax: (718) 982-3643 Email: [email protected]
Running title: Sonority-based segmentation 2
Abstract
The present study explored the use of linguistic knowledge in on-line word segmentation using
the word-spotting task. Native English-speaking listeners heard words such as absent embedded
in nonsense strings such as jeemabsent and jeebabsent in phonetically controlled materials.
Results showed that listeners more readily spotted targets following intervocalic sonorants than
intervocalic obstruents, suggesting a sonority-based preference in their parsing of the signal. It is
argued that this syllabification-like behavior is inconsistent with the notion that syllable-based
segmentation is determined by rhythm class. Word-level prosody, but not gradient phonotactics,
was also found to influence segmentation performance.
Running title: Sonority-based segmentation 3
1. Introduction
1.1. The problem of word segmentation
The present study concerns itself with the kinds of linguistic information listeners bring to bear
on the problem of word segmentation—i.e., the identification of word boundaries in the
continuous speech stream. Much of what we have learned about how (adult) human listeners
accomplish this task has come from the word-spotting paradigm (Cutler & Norris, 1988;
McQueen, 1996). In a word-spotting experiment, listeners must identify a word such as apple
embedded in a nonsense string such as vuffapple, and do so as quickly as possible. This task is
informative as to listeners’ segmentation of the string because performance depends crucially on
a unique parsing of that string (in the present case, vuff.apple rather than vu.fapple or vufa.pple).
When speakers provide useful phonetic cues to boundaries, these parsings can be made based on
the signal (Shatzman & McQueen, 2006; Newman, Sawusch, & Wunnenberg, 2011). However,
even without such cues, listeners segment the signal non-randomly, using knowledge about
statistically-dominant word-level prosodic patterns in their lexicon (the “Metrical Segmentation
Strategy”; Cutler & Norris, 1988) and knowledge of what constitutes a phonotactic violation
(McQueen, 1998; Norris, McQueen, Cutler, & Butterfield, 1997; see also Hanulikova, McQueen,
& Mitterer, 2010). Thus an important part of the solution to the segmentation problem lies in
non-signal-based knowledge and experience.
While these basic lexical-based biases are likely language-universal, another knowledge-
based strategy is claimed to be language-specific: the use of syllable structure. One of the earliest
findings in the study of spoken word segmentation was that French, but not English-speaking
listeners show segmentation biases that look very much like syllabifications. In their classic
findings, Cutler, Mehler, Norris, & Segui, (1986) demonstrated this asymmetry using the
Running title: Sonority-based segmentation 4
sequence monitoring task, finding that French listeners detected “BA” more quickly in ba.lance
than in bal.con, and “BAL” more quickly in bal.con than in ba.lance. Native English speakers,
however, showed no such “cross-over” pattern. It has since been argued (e.g., Cutler, McQueen,
Norris, & Somejuan, 2001) that this difference in the use of the syllable reflects the langauge’s
rhythm class. French is syllable-timed, and so segmentation can exploit syllable structure;
English is stress-timed, and so the syllable-based segmentation routine is unavailable.
1.2. Present study: sonority effects on word segmentation
The present study reexamines this rhythm class hypothesis by probing English-speaking listeners
for sensitivity to syllable structure in word-spotting. Specifically, the following question was
asked: are listeners’ on-line segmentations influenced by sonority sequencing preferences? This
was of interest because sonority-based preferences are known to be active in the grammar of
English speakers (Berent, Steriade, Lennertz, & Vaknin, 2007) in a way that depends on
syllabification of the signal (Daland, Hayes, White, Garellek, Davis, & Norrmann, 2011).
Importantly, sonority strongly influences explicit syllabifications for both French (e.g., Content,
Kearns, & Frauenfelder 2001; Goslin & Frauenfelder, 2002) as well as English-speaking subjects
(Treiman & Danis, 1988; Moreton, Feng, & Smith, 2008), in line with the cross-linguistic
generalization that well-formed syllables tend to have a sonority profile that rises maximally
from onset to nucleus and falls maximally from nucleus to coda (Sievers, 1881; Jespersen, 1904;
Hooper, 1976; Steriade, 1982; Selkirk, 1984; see also Murray & Venneman, 1983; Venneman,
1988; Clements, 1990).
Running title: Sonority-based segmentation 5
To test whether such a preference is active in the on-line segmentation routine used in English,
the experiment presented below tested listeners’ ability to spot a word like absent in (1a) versus
(1b):
(1) a. jeemabsent (/ʤimæbsәnt/)
b. jeebabsent (/ʤibæbsәnt/)
If listeners are sensitive to sonority in on-line segmentation as they are in off-line syllabification
tasks, they should spot absent more quickly in (1a) relative to (1b), since they will prefer to parse
the /m/ as a coda, rather than as an onset to the next nucleus. Conversely, in (1b), listeners’ bias
should be in the direction of parsing the obstruent /b/ as an onset rather than a coda, requiring a
re-parse in order identify the vowel-initial word absent. Importantly, it is not clear how the
lexical strategies discussed above could be employed by listeners, as neither a word boundary
before or after the crucial consonant would produce a phonotactic violation, and stress patterns
(i.e., primary stress on the second syllable’s nucleus) do not differ. Recently, an off-line study of
word-segmentation has demonstrated sensitivity to sonority patterns (in consonant clusters) using
an artificial language task (Ettlinger, Finn, & Hudson-Kam, 2012). The contribution of the
present study is to test whether similar sonority sequencing biases influence listeners on-line, as
part of the pre-lexical segmentation routine.
2. Methods
2.1. Stimuli
2.1.1. Design
Stimuli for a word-spotting experiment were based on a set of 50 disyllabic, vowel-initial
English target words: 25 with a strong-weak (SW) and 25 with a weak-strong (WS) stress
Running title: Sonority-based segmentation 6
Table 1. Example of SW and WS target words in each of the four preceding vowel and sonority conditions.
CV Context Obstruent Sonorant Target Stress Tense Lax Tense Lax /ʤib/absent /nɪb/absent /ʤim/absent /nɪm/absent /fuʃ/echo /fʊʃ/echo /fum/echo /fʊm/echo
/zeɪf/oven /zεf/oven /zeɪn/oven /sεn/oven SW /vuf/expert /væf/expert /vɑm/expert /væm/expert
/vus/ugly /vʊs/ugly /vun/ugly /vʊn/ugly /zɔɪg/ankle /zʌg/ankle /zɔɪn/ankle /zʌn/ankle /muʃ/average /mɪʃ/average /mul/average /gɛl/average
/ʃɑv/eject /ʃɪv/eject /zɑm/eject /zεm/eject /gig/enough /nʌg/enough /blim/enough /nɪm/enough /zeɪf/obsessed /zεf/obsessed /zeɪm/obsessed /zεm/obsessed
WS /zɔɪg/exert /zʊg/exert /zɔɪn/exert /zʊn/exert /bus/against /bʊs/against /fun/against /fʊn/against /vif/agree /vɪf/agree /vin/agree /vɪn/agree /fuʃ/occur /fʊʃ/occur /fun/occur /fʊn/occur
pattern, such as absent and eject, respectively. Target words were chosen that contained no
additional words embedded within them. For each target, two types of CVC syllables were
designed to precede it, one with a tense (/i,u,ɑ,o,eɪ,aɪ,oɪ/) and one with a lax vowel (/ɪ,ɛ,æ,ʌ,ʊ/).
The final C in the CVC (which, when appended on to the vowel-initial targets words, would
become an intervocalic consonant), fell into one of two sonority conditions: sonorants (/m,n,l/),
and obstruents (/b,d,g,ʤ,θ,f,s,ʃ,v,ð,z/). Obstruents were selected for their lack of informative
allophonic variation. Table 1 shows a subset of the 50 target words and their CVC contexts; for a
full list of all 200 stimuli items used (50 words 2 vowel tenses conditions two sonority
conditions), see [submitted_as_supplementary_materials].
Running title: Sonority-based segmentation 7
Additionally, 360 fillers were designed that contained no actual English words (e.g.,
/kʊˈvimɛp/), but resembled test items in other respects (i.e., equally divided among the same
sonority, vowel tenseness and stress pattern conditions).
2.1.2. Creation Auditory versions of the CVC and Target stimuli were then created, produced by a native
English speaker. Because the primary research question involved the effect of sonorant versus
obstruent consonants, it was necessarily the case that, across experimental conditions, the target
words would follow different consonants. It was therefore not possible to perform the cross-
splicing often used in word-spotting experiments to control for differences in the productions of
the targets themselves across conditions. That is, the absent from jeebabsent cannot be
segmented and spliced onto the absent in jeemabsent, due to coarticulatory effects, such as
carryover nasalization. Therefore, special care was taken to create stimuli that (a) would lack
speaker-encoded cues to intended syllabifications, and (b) would be unlikely to differ across
conditions in terms of duration. After producing stimuli carefully, it would then be possible to
“control” for any remaining differences, at least durational ones, by adding them to the statistical
model.
To this end, broad phonemic transcriptions of the stimulus materials were read and recorded
by a 23 year-old female speaker of Californian English (where /ɔ/ and /ɑ/ have merged, and the
remaining vowel, /ɑ/, patterns as tense). Recording took place in a sound-attenuating booth,
digitized at 22.05 kHz. To ensure that the consonants that would straddle the CVC-target
boundary would have phonetically ambiguous syllabic affiliation, these consonants were
produced as both codas to the CVC syllables, as well as onsets to the following target words.
This was accomplished by recording CVCs (e.g., jeeb, jeem) that were produced separately
Running title: Sonority-based segmentation 8
Figure 1. Example materials for stimulus creation. The highlighted portion of the CVC syllables was spliced on to the highlighted portion of the C+Target string to create jeebabsent and nibabsent. Stimuli for the sonorant consonant condition were created in the same way.
from target strings, and target strings like absent were produced with these same consonants as
onsets (e.g., babsent, mabsent).
A large number of versions of both CVCs like jeeb and jeem, as well as C+target strings like
mabsent and babsent were then produced and recorded, and the most durationally similar among
them selected. Then, to create the final stimuli, the CVC contexts were spliced onto the C+target
strings to produce, for example jeebabsent [ʤiˈbæbsәnt] and jeemabsent [ʤiˈmæbsәnt]. Splicing
was done (at zero-crossings) so that the consonant recorded as an onset to the target would
become the intervocalic consonant. Said otherwise, what remained in the final stimuli was the
CV portion of the recorded CVC context, followed by the C+target production (see Figure 1).
Thus, any coarticulatory information in the CV’s vowel should have been that of a tautosyllabic
Running title: Sonority-based segmentation 9
CVC Target
86
614
610
71245
220 Obstruent Condition
Sonorant Condition
j e e m a b s e n t
j e e b a b s e n t
Figure 2. Mean durations (in milliseconds) for the initial CV contexts, intervocalic consonants, and target words in each of the two sonority conditions.
coda consonant, but any (potential) phonetic cues to its syllabic affiliation during or after closure
would have suggested a syllable-initial consonant. This also created stimuli that were
durationally similar across the crucial sonority conditions, particularly with respect to targets
themselves, which differed by only 4 ms on average (see Figure 2).
Vowel tenseness conditions were created by simply splicing the same production of the
target from the tense vowel condition onto the CVC context with a lax vowel, within each
sonority condition. That is, the same production of mabsent was used in both [ʤimæbsәnt] and
[nɪmæbsәnt], and the same production of babsent was used in both [ʤibæbsәnt] and [nɪbæbsәnt];
since the vowel tenseness manipulation was orthogonal to consonant manipulation, the same
consonant could be used. The 360 filler items were similarly recorded, although produced intact
(i.e., no splicing took place).
2.2. Participants
Participants were 33 native speakers of American English, mostly undergraduates from
California, who received course credit or monetary compensation.
Running title: Sonority-based segmentation 10
2.3. Procedure
Participants served as listeners in a word-spotting experiment. Testing took place in a sound-
attenuating booth, over two sessions lasting approximately 20 minutes each. Participants were
told that they would hear a list of nonsense strings of speech, some of which would contain an
English word. Their task was to indicate when they heard a real English word by clicking a
computer key as soon as quickly as possible; immediately after pushing the key, they were to
then say that word aloud. If no word was heard in a string, subjects were to give no response, and
wait for the next trial. A brief practice session familiarized participants with the task and nature
of the stimuli. A within-subject design was used, with all participants hearing all targets in all
conditions. Stimulus items were divided into blocks such that a target word occurred once in
each block, in a different condition; blocks were counterbalanced with respect to tenseness and
sonority conditions, and fillers were distributed among the blocks. Stimuli were presented with
an ISI of 3 seconds by a MATLAB script that collected RTs. Participants’ verbal responses were
also recorded for each trial to assess accuracy.
2.4. Results
Data were dropped from four participants who either did not follow instructions or did not return
for their second session, leaving twenty-nine participants whose accuracy and RT were analyzed.
Accurate responses (defined as in Norris et al., 1997; 204) within 2 standard deviations of the
mean RT were used for analyses.
Results for all conditions are shown in Table 2. To better understand these patterns, RTs and
accuracy were modeled using mixed-effects linear and logistic regression, respectively. In
addition to random intercepts for participant and item, initial models contained the following
Running title: Sonority-based segmentation 11
Table 2. Average RT (in ms, measured from target offset) and accuracy for strong-weak and weak-strong target words in the four experimental conditions.
Strong-Weak Targets
Tense Vowel Lax Vowel
Context Context Obstruent C Context
RT 403 414 Accuracy 87.60% 89.60% Example /ʤib/absent /nɪb/absent
Sonorant C Context RT 383 392 Accuracy 93.30% 91.90% Example /ʤim/absent /nɪm/absent
Weak-Strong Targets
Tense Vowel Lax Vowel
Context Context Obstruent C Context
RT 363 347 Accuracy 90.30% 92.00% Example /ʃɑv/eject /ʃɪv/eject
Sonorant C Context RT 345 350 Accuracy 94.30% 94.70% Example /zɑm/eject /zɛm/eject
fixed-effects factors: preceding vowel tenseness, sonority of the intervocalic consonant, and
stress; stimulus-level predictors were: target duration, duration of the intervocalic consonant,
duration of preceding CV, and presentation of the target (i.e., 1st, 2nd, 3rd or 4th time the
participant had encountered that particular target word during the experiment). In initial models
the three linguistic variables were permitted to interact with one another, and with presentation.
Non-contributing factors in the models were identified (using log-likelihood ratio tests) and
removed from the final refitted models.
The results of the best fit models of RTs and accuracy are shown in Tables 3 and 4,
respectively. First, presentation was significantly associated with lower RTs and higher
accuracy, indicating that, unsurprisingly, listeners were better at spotting a target word with each
Running title: Sonority-based segmentation 12
Table 3. Estimate, standard error, t-values and p-values from the model of reaction times.
Table 4. Estimate, standard error, z-values and p-values from the logistic regression model of accuracy.
β SE z p (Intercept) -1.96 0.70 -2.79 < 0.01 Presentation 0.83 0.06 14.71 < .0001 Target duration 4.89 1.10 4.45 < .0001 Sonority (+son) 0.61 0.11 5.55 < .0001
successive time they encountered it during the experiment; the significant effect of target
duration indicated that longer target words were also spotted more successfully.
More interesting were the results for linguistic variables. Notably absent from models of both
speed and accuracy was the tenseness of the vowel in the CVC syllables—indicating this did not
influence segmentations. There was also a significant effect for stress, such that SW targets were
spotted more quickly (though not more accurately) than WS targets. Note that this is not easily
seen from looking at averages in Table 2, where WS words appear to be associated with shorter
RTs. This discrepancy results from the fact that WS targets were on average longer than SW
words, which is factored out by the model.
Crucial to the present purposes, there was also a significant effect for sonority, indicating that
targets were spotted more quickly and more accurately when they followed sonorants. On
average (collapsing across vowel tenseness conditions), targets were identified 15 ms faster when
they followed a sonorant. Although there was no significant interaction with presentation
β SE t p (Intercept) 1012.15 30.50 33.18 < .0001 Presentation -46.85 1.89 -24.84 < .0001 Target Duration -765.52 33.74 -22.69 < .0001 Sonority (+son) -18.81 7.25 -2.59 < .01 Stress (SW) -52.27 8.59 -6.09 < .0001
Running title: Sonority-based segmentation 13
(indicating that sonority’s effect was robust even on later trials), the advantage was numerically
largest for first (22ms) and second (24ms) presentations.
The results were further probed for a gradient effect of sonority on the parsing of intervocalic
consonants. If such an effect were present, we might expect that, for example, spotting absent
would be easier in a string like jeevabsent than in jeebabsent, since /v/ is a high-sonority
obstruent relative to /b/, given commonly assumed sonority hierarchies, such as the one in (2):
(2) Sonority scale for obstruents (e.g., Parker, 2002):
/ p t k ʧ / < / b d g ʤ / < / f θ s ʃ / < / v ð z ʒ /
The stimuli in the present experiment were not designed to address this question, since different
targets were paired with different obstruent consonants (i.e., we would be comparing
jee[b]absent versus vu[ʃ]echo versus chi[v]onion, etc). However, since the stimuli used in the
obstruent condition made use of an array of consonants (and multiple targets were paired with
each), it was possible exploit the data to address this question in post-hoc fashion.
RTs for target words were therefore binned according to their preceding intervocalic obstruent,
and mean RT calculated for each bin of targets (this analysis was limited to RTs, and for first
presentations only). Figure 3 shows the results of this binning, ordered according to average RT
for each bin. A second round of modeling indicated that, when target duration was taken into
account, the only noteworthy difference was a marginally significant one between targets
following /g/ and those following /v/ (β = 144.9, p=.0996). However, given the lack of control
over the target words in each group, we might have expected this to come out much more
randomly than it did; as Figure 3 shows, the numerical parsability of obstruents as codas very
much resembled the sonority scale, such that higher sonority tends to be associated with coda
parsings. A further, larger study would be needed to explore this trend further.
Running title: Sonority-based segmentation 14
0
100
200
300
400
500
600
/g/ /θ/ /d/ /ʤ/ /b/ /f/ /ʃ/ /s/ /v/ /ð/ /z/
Intervocalic Consonant
Ave
rage
RT
(ms)
(6) (6) (4) (2) (4) (9) (6) (4) (4) (2) (2)
Figure 3. Mean reaction times to targets, grouped by the obstruent consonant they followed (rank-ordered from slowest to fastest). Error bars show standard error; number in parentheses indicates the number of targets that followed that particular consonant.
3. Discussion and Conclusion
The experiment presented here tested whether English-speaking listeners showed a bias that
could be understood in terms of syllable structure. The results showed very clearly that such a
bias exists; when positing word boundaries, listeners do not treat all intervocalic consonants
equivalently. Rather, they prefer to parse sonorants with the preceding vowel (jeem.absent), and
obstruents with the following vowel (jee.babsent), making vowel-initial words more difficult to
spot in the latter case. Thus, on-line segmentations by English-speakers show the same
preference that off-line syllabifications show, which also reflect cross-linguistic preferences for
sonority profiles for syllables. This is inconsistent with the claim that native language rhythm
class dictates the utilization of such a strategy, since listeners of English––the prototypically
stress-timed language––seem to be guided by syllable structure as well. There was also some
Running title: Sonority-based segmentation 15
suggestive evidence that sonority’s effect on parsing was gradient, as treatment of obstruents was
broadly consistent with the sonority scale. This finding, though interesting, requires further
testing (with more statistical power) to be verified.
One question is whether these sonority-based preferences could actually be explained by the
same mechanisms as other findings from word-spotting studies, i.e., lexical statistics, after all
(Cairns, Shillcock, Chater, & Levy, 1997; Daland & Pierrehumbert, 2011). The models in the
present study did not include estimations of the probabilities that various consonants appear in
word-initial or word-final positions. Therefore, it may be that the sonority scale and gradient
lexical statistics make similar predictions, in which case the segmentation mechanism could
possibly have only an indirect relation to sonority. Of course, this would not explain the relation
between sonority (as a phonetic concept) and such a structuring of the lexicon—or the cross-
linguistic generalizations that require reference to sonority sequencing.
While this matter is left open for further study, it should be pointed out that such gradient
phonotactic knowledge has generally not been shown to influence segmentation in word-
spotting, possibly distinguishing this on-line task from off-line acceptability-judging (see
Albright, 2009). Notably, listeners in the present study were not found to avoid even
segmentations that would leave a preceding CV sequence with a lax vowel unparsed, a
categorically unattested form in English. This same finding has been reported by Norris et al.
(2001), and by Newman et al. (2011), who note the apparent non-penalty for ill-formed (but
syllabic) residues may indicate that phonotactic information only becomes available post-
perceptually. They also note the possibility that violations on residues may be less crucial than
those on target onsets, consistent with previous findings (McQueen 1998), and echoing claims
about the primacy of onsets over codas in segmentation (van der Lugt 2001; Dumay,
Running title: Sonority-based segmentation 16
Frauenfelder, & Content, 2002). Therefore, while it is clear from the present study that rhythm
classes are not necessary to predict basic syllabic-parsing, such mechanisms may be qualified by
independent constraints inherent to the parser.
Acknowledgements
[Acknowledgements to be added post-review].
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Supplementary Materials
Complete list of target words and CVC conditions used in the word-spotting experiment. Targets are shown in English orthography, initial CVC syllables in broad IPA transcription.
(SW Targets) Obstruent Sonorant
Tense Lax Tense Lax /dɑv/over /mɪv/over /dɑm/over /mɪm/over /dɔɪg/image /dεg/image /dɔɪm/image /dεm/image /fɔɪz/under /fεz/under /fɔɪm/under /fɪm/under /fuð/album /vʊð/album /fun/album /vʊn/album /fuʃ/echo /fʊʃ/echo /fum/echo /fʊm/echo /keɪθ/angle /kεθ/angle /keɪl/angle /kεl/angle /kus/empty /kʊs/empty /kum/empty /kʊm/empty /lɔɪb/expect /lʌb/expect /lɔɪm/expect /lʌm/expect /muʃ/average /mɪʃ/average /mul/average /gɛl/average /neɪs/effort /nεs/effort /neɪn/effort /nεn/effort /θeɪf/enter /vɛf/enter /zin/enter /næn/enter /θeɪf/extra /θεf/extra /θeɪm/extra /θεm/extra /pɔɪd/uncle /dʊd/uncle /pɔɪn/uncle /dʊn/uncle /ʧɑθ/ancient /ʧʌθ/ancient /teɪn/ancient /tʊn/ancient /ʧiv/onion /ʃɪv/onion /plul/onion /plεl/onion /vif/anxious /vʌf/anxious /vin/anxious /vεn/anxious /vub/other /vʊb/other /vul/other /vʌl/other /vuʤ/ever /vεʤ/ever /vum/ever /vεm/ever /vuf/expert /væf/expert /vɑm/expert /væm/expert /vuθ/either /gʊθ/either /vul/either /gʊl/either /vus/ugly /vʊs/ugly /vun/ugly /vʊn/ugly /zeɪf/oven /zεf/oven /zeɪn/oven /sεn/oven /zeɪv/action /zæv/action /zɑm/action /zæm/action /zɔɪg/ankle /zʌg/ankle /zɔɪn/ankle /zʌn/ankle /ʤib/absent /nɪb/absent /ʤim/absent /nɪm/absent
(WS Targets)
Obstruent Sonorant Tense Lax Tense Lax /bus/against /bʊs/against /fun/against /fʊn/against /ʤaɪf/accept /ʤæf/accept /ʤaɪm/accept /gæm/accept /dɔɪg/allow /dʊg/allow /dɔɪm/allow /dεm/allow /faɪʤ/attach bʊʤ/attach /faɪm/attach /fæm/attach /fug/astound /fεg/astound /fum/astound /fεm/astound /fuʃ/occur /fʊʃ/occur /fun/occur /fʊn/occur /gib/immerse /gɪb/immerse /θul/immerse /dæl/immerse /giʤ/annoy /gʌʤ/annoy /zin/annoy /zʊn/annoy
/gig/enough /nʌg/enough /blim/enough /nɪm/enough /giz/erect /gεz/erect /gin/erect /gɪn/erect /keɪθ/among /kεθ/among /bul/among /ʧʊl/among /θeɪʃ/adopt /θεʃ/adopt /θeɪn/adopt /θεn/adopt /θeɪʃ/exist /θʌʃ/exist /θeɪn/exist /θʌn/exist /ʃɑv/eject /ʃɪv/eject /zɑm/eject /zεm/eject /ʧeɪð/exempt /ʧɪð/exempt /ʧeɪm/exempt /kεm/exempt /teɪθ/applaud /tæθ/applaud /teɪn/applaud /gεn/applaud /vif/agree /vɪf/agree /vin/agree /vɪn/agree /voʊd/avenge /vɪd/avenge /voʊn/avenge /vɪn/avenge /vuʤ/effect /vʊʤ/effect /vul/effect /vʊl/effect /vuθ/achieve /væθ/achieve /vul/achieve /fæl/achieve /vuʃ/object /vʊʃ/object /vul/object /vʊl/object /zeɪf/obsessed /zεf/obsessed /zeɪm/obsessed /zεm/obsessed /zɔɪg/exert /zʊg/exert /zɔɪn/exert /zʊn/exert /zif/elect /zɪf/elect /zin/elect /zɪn/elect /vuʃ/evict /vʊʃ/evict /vum/evict /vʊm/evict