Download Effects of Lexical Factors on Word Recognition Among Normal

J Am Acad Audiol 12 : 233-244 (2001)
Effects of Lexical Factors on Word
Recognition Among Normal-Hearing
and Hearing-Impaired Listeners
Donald D . Dirks*
Sumiko Takayanagi*
Anahita Moshfegh'
Abstract
An investigation was conducted to examine the effects of lexical difficulty on spoken word
recognition among young normal-hearing and middle-aged and older listeners with hearing
loss . Two word lists, based on the lexical characteristics of word frequency and neighborhood density and frequency (Neighborhood Activation Model [NAM]), were developed : (1)
lexically "easy" words with high word frequency and a low number and frequency of words
phonemically similar to the target word and (2) lexically "hard" words with low word frequency
and a high number and frequency of words phonemically similar to the target word . Simple
and transformed up-down adaptive strategies were used to estimate performance levels at
several locations on the performance-intensity functions of the words . The results verified
predictions of the NAM and showed that easy words produced more favorable performance
levels than hard words at an equal intelligibility . Although the slopes of the performanceintensity function for the hearing-impaired listeners were less steep than those of normal-hearing
listeners, the effects of lexical difficulty on performance were similar for both groups .
Key Words: Lexical stimuli, simple and transformed up-down adaptive strategies, word recognition
Abbreviations : ANOVA = analysis of variance, CVC = consonant-vowel-consonant, NAM =
Neighborhood Activation Model, NU-6 = Northwestern University Auditory Test No . 6, SNR
= single-to-noise ratio
ost theories of spoken word recognition
involve several underlying perceptual
processes in which the speech signal
converted
into an acoustic-phonetic repreis
normalized
for factors such as difsentation,
ferences among talkers, speaking rate, and
dialects and then matched to one of the thousands of items stored in long-term memory.
Regarding the last step in this process, it has
been shown that several lexical factors, including the frequency of occurrence of a word in
M
*National Center for Rehabilitative Auditory Research,
Veterans Administration Medical Center, Portland, OR ;
!Veterans Administration Greater Los Angeles Healthcare
System ; 'Division of Head and Neck Surgery, UCLA School
of Medicine, Los Angeles, California
Reprint requests : Donald D . Dirks, Division of Head
and Neck Surgery, UCLA School of Medicine, Rehabilitation
Center, Bldg 31-24, Los Angeles, CA 90095
the lexicon and the number and frequency of
other words phonemically similar to the target,
affect the speed and accuracy of spoken word
recognitions (Luce, 1986). The influence of these
lexical factors on word recognition has been
addressed in the Neighborhood Activation Model
(NAM), which assumes that words that occur
frequently and have few phonemically similar
neighbors (lexically "easy" words) are recognized more accurately than words that occur less
frequently but have a large number of phonemically similar neighbors (lexically "hard"
words) . Most empirical support for the model has
been found among young normal-hearing listeners (Luce, 1986 ; Sommers, 1996 ; Luce and
Pisoni, 1998 ; Dirks et al, 2001). In addition,
experimental results have also indicated that
the NAM's principles can be generalized to children fitted with cochlear implants (Kirk et al,
1995), adults with acquired sensorineural hearing loss (Kirk et al, 1997 ; Dirks et al, 2001), and
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Journal of the American Academy of Audiology/Volume 12, Number 5, May 2001
non-native listeners with normal hearing (Bradlow and Pisoni, 1999). Recognition performance,
in the aforementioned investigations, has routinely been measured at a single speech presentation level or signal-to-noise ratio (SNR),
frequently selected at or near ceiling levels for
word recognition. Thus, comparative results
between easy and hard word recognition are necessarily limited to one estimate on the underlying performance-intensity functions. As will
be reviewed in detail, the results in all studies
show higher performance for easy versus hard
word recognition; however, the magnitude of
the effect varies . To examine the effects of these
lexically based words on speech recognition in
a more comprehensive manner, in this investigation, intelligibility was measured at several
locations on the response curves for easy and
hard words among listeners with normal hearing and hearing loss .
The NAM proposes that discrimination and
identification of a particular target word are
influenced by several lexical factors. Central to
the model is the concept that words in the mental lexicon are organized into similarity neighborhoods, which can be created from the target
item by adding, deleting, or substituting a single phoneme. Thus, words such as "nap," "sap,"
"lap," and "mad" would be considered neighbors
of the word "map ." A similarity neighborhood is
composed of two structural characteristics: (1)
neighborhood density or the number of words
that are phonemically similar to a particular target item and (2) neighborhood frequency, which
refers to the average frequency of occurrence of
items in the neighborhood . The frequency of
occurrence of a word in the language has often
been based on word frequency counts such as
those provided by the Brown Corpus of printed
text (Kucera and Francis, 1967). Target words
with many neighbors (phonemically similar
words) are located in "dense" neighborhoods
and those with few neighbors in "sparse" neighborhoods . A third lexical factor, the frequency of
occurrence of the target word itself, is considered
a biasing factor, making it easier to recognize a
word that occurs often in language as opposed
to one that is seldom used . Although neighborhood density, neighborhood frequency, and word
frequency each significantly influence word
recognition, any one of these lexical properties
is independently insufficient to account for the
effect of lexical discrimination on word recognition. Rather, words are recognized relationally,
that is, word frequency and the neighborhood
structural effects of neighborhood density and
234
average neighborhood frequency covary (Meyer
and Pisoni, 1999 ; Dirks et al, 2001). Further
theoretical details of NAM have been described
by Luce and Pisoni (1998) .
According to NAM, words with high word
frequency and low neighborhood density and
neighborhood frequency should be identified
more accurately than familiar words with low
word frequency and high neighborhood density
and neighborhood frequency. In investigations
by Luce and Pisoni (1998) and Dirks et al (2001),
the predictions from NAM theory were tested
comprehensively by measuring performance for
experimental conditions that were formed
orthogonally by combining two levels of stimulus and frequency (high and low), neighborhood
density (high and low), and average neighborhood frequency (high and low) . Combining the
three independent lexical variables and two levels resulted in eight possible lexical conditions .
In the Luce and Pisoni (1998) study, subjects
were normal-hearing listeners, whereas Dirks
et al (2001) included normal and hearingimpaired listeners . Dirks et al (2001) attempted
to minimize ceiling effects by word presentations at SNRs or speech presentation levels
where performance resulted in scores between
40 and 70 percent correct . Although the recognition scores for normal and hearing-impaired
listeners varied slightly, the results demonstrated that the highest performance scores
were always obtained for the high word frequency and low neighborhood density and neighborhood frequency condition . For both groups of
subjects, scores from this condition were, on
average, 15 percent higher than scores from the
condition with the lowest performance scores, the
low word frequency and high neighborhood density and neighborhood frequency condition . In the
Luce and Pisoni (1998) study, the aforementioned conditions also produced the highest and
lowest scores . Because of the large and consistent difference in performance between these
conditions for both normal and hearing-impaired
listeners, in the current experiment, lexically
easy words were derived from a database of consonant-vowel-consonants (CVCs) that were used
to form the high word frequency and low neighborhood density and neighborhood frequency
condition, whereas lexically hard words were
those incorporated in a database from the low
word frequency and high neighborhood density
and neighborhood frequency condition .
The definition of lexically easy and hard
words has varied somewhat among investigators, some stressing word frequency and neigh-
Effects of Lexical Factors/Dirks et al
borhood density (Kirk et al, 1997) and others
neighborhood density and neighborhood frequency, with word frequency itself controlled
(Sommers, 1996 ; Sommers et al, 1997) . In the
studies by Luce and Pisoni (1998), Dirks et al
(2001), and Bradlow and Pisoni (1999), the three
lexical factors were varied to form the experimental conditions . Regardless of the variations
in definition, however, performance scores for
lexically easy words have always been found to
be superior to those for hard words, although the
difference in scores between the easy and hard
word lists has varied .
As indicated earlier, in experiments concerned with the effects of lexical difficulty on
word recognition, the stimuli have been administered at a single presentation level or SNR . As
a consequence, performance could only be evaluated from one location on each of the underlying performance-intensity functions . In the
previously cited experiments, it was not unusual
to present the speech at a high comfortable presentation level or favorable SNR . Thus, performance ceiling effects probably influenced the
difference in scores between the lexically easy
and hard word conditions in such experiments .
For hearing-impaired listeners, the problem of
choosing the appropriate speech presentation
level to avoid ceiling or floor effects is especially
difficult because of the large variations in the
magnitude of the hearing loss and the audiometric configuration . Even individuals with similar magnitudes of hearing loss may have
different performance-intensity functions and
maximally obtainable speech recognition scores .
In the current investigation, simple and
transformed up-down adaptive strategies (Levitt,
1971) were used to estimate 29 .3, 50 .0, and 70 .7
percent correct on the response curves for easy
and hard words . Thus, performance was measured at equal intelligibility for easy and hard
word recognition among both normal-hearing
and hearing-impaired subjects . There are at
least two advantages in choosing adaptive strategies for this investigation . First, performance is
measured at equal intelligibility on the response
curve for all subjects . Thus, at least one common
performance reference exists for comparison
between results from normal-hearing versus
hearing-impaired individuals . Second, by choosing adaptive strategies that measure word recognition at points between -30 and 70 percent
(the linear portion of the response curve), it is
possible to minimize the risk of measuring performance at presentation levels where scores
begin to transition from the linear to the non-
linear segment of the performance function,
often making comparisons between group results
problematic .
The primary purpose of this investigation
was to examine the performance-intensity
functions for lexically easy and hard words
for normal-hearing subjects and individuals
with mild-to-moderate sensorineural hearing
loss . Because the data of Sommers (1996) suggested that age itself may affect the ability to
isolate an individual lexical representation
from among phonetically similar competitions,
half of the hearing-impaired subjects in the current investigation were middle-aged between
33 and 60 years and the other half were older
listeners between the ages of 61 and 80 years .
Scores were obtained using adaptive strategies,
which converged on 70 .7, 50 .0, and 29 .3 percent response points on the performance-intensity curves . Thus, comparisons could be made
between performance for the lexically easy
and hard words at several locations on the
response curves where ceiling and floor effects
were absent or minimized .
METHOD
Participants
Participants in the study were 20 adults
with normal hearing and 20 adults with sensorineural hearing loss . The 20 adults (10 male
and 10 females) with normal hearing had a
mean age of 22 years (range 18-33 years) and
were recruited from the student and employee
population at UCLA . These subjects had normal hearing (<_15 dB HL at octave test frequencies from 250 to 6000 Hz) and scored 94
percent or higher on the Northwestern University Auditory Test No . 6 (NU-6) (Department of Veterans Affairs, Version 1 .1, 1991)
with words presented at 40 dB SL re : spondee
threshold . The 20 hearing-impaired adults (7
male and 13 females) had a mean age of 62
years (range 33-81 years) . Half of the subjects
were less than 60 years, and half were over 60
years of age . The average age for the middleaged subgroup was 50 years and for the older
subgroup was 75 .1 years . Subjects with mildto-moderate sensorineural hearing loss were
recruited from patients seen for otologic and
audiologic assessment at the UCLA Medical
Center or the Veterans Administration Greater
Los Angeles Healthcare System . Among the
older participants (>60 years), the etiology of
the hearing loss was diagnosed either as pres-
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Journal of the American Academy of Audiology/Volume 12, Number 5, May 2001
bycusis or as presbycusis combined with noiseinduced hearing loss . Among the 10 middleaged patients, 5 had a history of noise exposure,
and the remainder had hearing loss of unknown
origin . All subjects denied serious neurologic
problems (such as stroke, central nervous system lesions, or cognitive disorders), and none had
been tested for central nervous system problems or serious mental disorders . For most subjects, the hearing loss was bilaterally symmetric .
If there was a difference between ears for an individual subject, the ear with the more sensitive
thresholds was tested during the experiment .
Table 1 presents the mean auditory test thresholds of subjects along with results from speech
audiometry. All subjects were native English
speakers and were paid for their participation .
imental conditions in which the three lexical
conditions were systematically manipulated . A
total of 114 CVC words from the database were
found to conform to these lexical characteristics
and were considered easy words for the current
study. The earlier experimental results also
demonstrated that words with low word frequency and high neighborhood density and frequency produced the poorest recognition scores .
A total of 123 CVC words that possessed these
lexical characteristics were found and considered
hard words.
From the available words in each lexical
category, 75 easy and 75 hard words were
selected for use in the current experiment. In
generating the specific 75 easy and 75 hard
words, attention was given to the types of
phonemes contained in each word list . Although
no attempt was made to phonetically balance the
words, each list contained a representative sample of phonemes . Because persons with hearing
loss may find special difficulty in the recognition
of certain consonants (e .g., unvoiced fricative),
an attempt was made to control the phonetic content of the words between the two lists. There
were, however, only a modest number of CVC
words available for each lexical group, so it was
not possible to equate the phonemic content
between the lists completely. Table 2 provides a
frequency count of the linguistic features for
the easy and hard word lists. The phonemic
content (percent of phonemes in the initial and
final positions) between the two lists varied
modestly, but the differences were not large.
The specific words used in this investigation
are reported in the Appendix.
Table 3 provides descriptive statistics concerning the lexical characteristics for the easy
Test Stimuli
Lexically easy and hard words were selected
using an online lexical database (the 20,000word Hoosier Mental Lexicon; Nusbaum et al,
1984) derived from Webster's 1967 Pocket Dictionary. The database contains orthographic
and phonetic transcriptions, a word frequency
count based on Kucera and Francis (1967), a subjective rating of familiarity (Nusbaum et al,
1984 ; the rating scale ranged from 1.0 "don't
know the word" to 7 .0 "know the word and its
meaning"), and quantitative estimates of neighborhood lexical density and mean neighborhood
frequency.
As indicated previously, earlier results (Dirks
et al, 2001) demonstrated that CVC words with
high word frequency counts and low neighborhood density and frequency always produced
the highest recognition scores among eight exper-
Table 1
Means and SDs for Auditory and Spondee Word Thresholds and
Speech Recognition Scores for Hearing-Impaired Listeners
Auditory Thresholds (dB HL)
Frequency (kHz)
0 .25
0.5
Middle-aged
hearing impaired
(n = 10)
16 .5 (12 .3) 17 .5 (10 .3)
Older hearing
impaired (n = 10) 18 .5 (9 .4) 20 .5 (12 .3)
Total hearing
impaired (n = 20) 17 .5 (10 .7) 19 .0 (11 .2)
236
1 .0
2.0
4 .0
6.0
Spondee
NU-6
Word
Recognition
Threshold Score (%)
25 .0 (12 .9)
33 .5 (9 .1)
47 .0 (9 .2)
52 .5 (4 .9)
25 .2 (8 .3)
28 .5 (17 .0)
38 .5 (10 .3)
55 .5 (11 .7)
67 .5 (10 .1)
28 .3 (12 .4) 90 .1 (7 .1)
26 .7 (14.8)
36.0(9 .8)
51 .2(11 .1)
60 .0(10 .9)
26 .8 (10 .4) 91 .4 (6 .1)
92 .8 (4 .7)
Effects of Lexical Factors/Dirks et al
Table 2
Summary of the Phonemic Content of the 75 Easy and 75 Hard Words
Initial Position (%)
Phoneme Type
IPA Symbol
Voiced plosives
Unvoiced plosives
/b/, /d/ ./g/
Unvoiced fricatives
Nasals
I fl, l0/, /s/, /S/, /hl
Voiced fricatives
Approximants
Voiced affricates
Unvoiced affricates
/P/. /t/, /k/
/v/, /8/ , / Z/
/m/, /n/, /rq/
/I/, /r/, /wL /j/
/tS/
/dZ/
Final Position (%)
Easy
Hard
Easy
Hard
20 .0
20 .0
22 .7
21 .3
9 .3
21 .3
13 .3
30 .7
10 .7
24 .0
33 .3
5 .3
1 .3
5 .3
5 .3
0 .0
0.0
2.7
0 .0
14 .7
14 .7
6 .7
10 .7
17 .3
24 .0
25 .3
5 .3
5 .3
0.0
6.7
6 .7
16 .0
*/h/ is grouped as fricative as per categorization by Ladefoged (1993) .
IPA = International Phonetic Alphabet .
and hard word lists . Words were selected so
that the median neighborhood density of the
easy word list was considerably lower than
the median neighborhood density of the hard
word list (15 .0 vs 26 .0) . The median neighborhood frequency for the easy words was also
much lower than the median neighborhood
frequency of the hard words (30 .6 vs 180 .4) .
Finally, median word frequency for the easy list
was much higher than the median word frequency of the hard list (77 .0 vs 6 .0) . Median
values were used in developing the word lists
rather than the mean because the distribution
of the individual lexical variables was not
always symmetric around the mean . In summary, the easy word list is characterized as a
set of words that occur frequently in the language and have low-frequency neighbors . In
contrast, the hard word list contained words
that occur much less frequently in the language than the easy words and have many
neighbors that are high in frequency relative
to the easy word list .
Table 3
Lexical Factors
Easy words
Median
Mean
Range
Hard words
Median
Mean
Range
Recording Procedure
An adult female talker with General American English recorded both the easy and hard
words. The talker was seated in a double-walled
sound attenuation room (Industrial Acoustics,
Model l204A) and read the words, from sheets
placed in front of her, at a normal conversational level.
The stimuli were transduced via a microphone (AKG, C460b, flat frequency response)
and associated amplifier onto a digital audiotape
recorder (Sony, DAT DTC-46) . The productions
were monitored by three laboratory staff, and
occasionally words were recorded several times
because of misarticulations or mispronunciations . The words stored on the audiotape were
digitized via a Computerized Speech Laboratory
System (CSL model-4300, Kay Electronic Systems) at a sampling rate of 25 kHz with 16-bit
resolution . The items were "down-sampled" to
24 kHz for compatibility with the sound processing board used to deliver the stimuli dur-
Descriptive Statistics for the Easy and Hard Word Lists
Word Frequency
Neighborhood Density
Neighborhood Frequency
77 .0
15 .0
30 .6
878 .0
16 .0
81 .5
141 .5
6 .0
6 .6
15 .0
14 .2
26 .0
27 .0
16 .0
36 .8
180 .4
276 .7
1009 .4
Median word frequency is reported in occurrences per million, median neighborhood density is the number of lexical neighbors, and
median neighborhood frequency is the average frequency of all of the neighbors.
237
Journal of the American Academy of Audiology/Volume 12, Number 5, May 2001
ing the experiment . The words were edited,
and the speech files were then converted to
WAV format and prepared for presentation .
The long-term root mean square (rms) spectrum of the talker was measured by playing a
concatenated string of the 75 easy or hard words
and averaged on a real-time analyzer (LarsonDavis 4300). The spectrum was typical of that
for female talkers. This particular talker had
recorded words for a previous study, and the
detailed spectral characteristics have been
reported (Dirks et al, 2001, Fig. 1) . Spectral
analysis of the word lists indicated that there was
essentially no difference between the long-term
rms level of the easy and hard words. In addition, the rms level of each individual word was
also measured using a software analysis program
(Cool Edit Pro Version 1 .0, 1997). From this
analysis, it was determined that the individual
words varied in rms level over a range of 6 dB .
This range is similar to that reported in a previous study (Dirks et al, 2001) for words spoken
under similar conditions and at a conversational
level. The speech levels for the isolated words
were essentially equivalent for the two lexical
lists.
Procedure
The words were stored on the hard disk of
a personal computer (Gateway E-3111) . An
in-house software program was developed to
deliver the words randomly via a digital signal
processing board (Starky HW DWP) during each
test trial. The software program automatically
adjusted the intensity level of the signal depending on the strategy for the specific adaptive procedure . Subjects sat in a double-walled sound
attenuation room for the test sessions . A routine
audiometric examination was conducted to determine auditory thresholds for pure-tone stimuli
followed by measurement of spondee threshold
and recognition ability for NU-6 words. For the
experimental tests, the speech signal was delivered to one channel of an audiometer (GrasonStadler 16), where the overall output level of the
signal could be adjusted, and then passed to a
single earphone (TDH-50 encased in a model 51
cushion) . The speech level was specified using
a calibrated tone in a NBS 9A coupler and the
output read on a sound level meter (LarsonDavis 800B) set for a "C" scale reading. The
subjects responded verbally after each word presentation, and the tester pressed designated
keys on the keyboard for a correct or incorrect
response, which then initiated a new trial. The
238
entire protocol was conducted within a 2-hour
test session with several rest periods .
As indicated earlier, adaptive procedures
were used to determine the speech performance
levels that estimated 29 .3, 50 .0, and 70 .7 percent
correct on the individual response curve. For estimating 50 percent correct, a simple up-down
procedure (Levitt, 1971) was used . Transformed
up-down methods, following the strategies
described by Levitt (1971, Table 1), were used
to estimate the 29 .3 or 70 .7 percent responses
on the curve . When applying the simple
up-down procedure to estimate 50 percent correct, the stimulus level was decreased by a fixed
amount if a positive response was obtained and
increased if a negative (incorrect) response
occurred . For the 29 .3 percent estimates, stimulus level was increased after two incorrect
responses and decreased either following a correct response or an incorrect/correct response
sequence . For the 70 .7 percent estimates, stimulus level was increased following either an
incorrect response or a correct/incorrect response
and decreased following two correct responses.
For both the simple and transformed up-down
procedures, 20 of the 75 words available (either
the easy or hard lists) were used to determine
an initial starting level that was the best prior
estimate of the intended outcome. A large step
size of 6 dB was used for this portion of the test,
followed by initiation of the experimental
sequence in which a 3-dB step size was employed .
For the experimental sequence, the initial run
(a run is a sequence of changes in stimulus level
in one direction only) was disregarded by the program, and the final estimate (calculated by the
software program) was based on the results
from the remaining runs . The test was terminated after responses were made to the remaining 55 of the 75 words available. Generally, a
final estimate was based on results from 12 or
more runs, slightly more for the simple up-down
procedure and less for the transformed up-down
procedure. Although this procedure was not the
most efficient use of the adaptive strategies, it
was decided to administer all of the words available to estimate a particular point on the
response curve. Hearing-impaired listeners often
have particular difficulty perceiving certain
phonemes (e .g., unvoiced fricatives) . If a particular randomization resulted in a sequence of
words heavily weighted with these phonemes,
the final estimate might have had an underlying bias . Thus, all 75 words from either the easy
or hard lists were used to obtain the final estimate level. Because the number of available
Effects of Lexical Factors/Dirks et al
CVCs that fell within the lexically easy or hard
criterion was modest (114 easy words, 123 hard
words), it was not possible to have a sufficient
number of different words for estimation of all
three performance levels on the response curve .
As a consequence, estimates for 29 .3 percent
correct (where many words were missed) was
always measured initially, followed by estimates
of 50 percent correct and finally 70 .7 percent correct . This procedure was followed to minimize
contamination of the estimates by correct guesses
based on knowledge of the test items . The rationale was that an item that is heard correctly at
a low level will generally be understood at a
higher stimulus level, so that the subject's estimate should not be significantly changed at one
stimulus level by the fact that he or she has
already identified the items at a lower presentation level . The presentation of the hard or
easy list was alternated among subjects so that
half started with the easy list while the other half
heard the hard words . For each estimate, a different randomization of the words was used . To
summarize, each subject provided an estimate
for the easy and the hard words to obtain the
stimulus levels for 29 .3, 50 .0, and 70 .7 percent
correct on the individual response curve .
Data Analysis
The overall analysis was conducted on the
measured performance levels and consisted of a
2 (group : normal hearing, hearing impaired) x
2 (lexical category : easy, hard words) x 3 (per-
formance conditions : 29 .3, 50 .0, and 70.7 percent)
mixed analysis of variance (ANOVA) . A post hoc
pairwise contrast analysis was carried out to
determine if the differences between easy versus hard words were significantly different for
either or both the normal and hearing-impaired
listeners at each performance level . Finally, a 3
(performance level) x 2 (groups) mixed ANOVA
was conducted on the difference scores between
easy and hard word recognition to determine if
there was any statistical difference in performance levels between normal and hearingimpaired subjects . Statistical significance was
evaluated at an alpha level of .05 .
RESULTS
igure 1 shows the overall mean perforF mance levels for easy and hard words at the
29 .3, 50 .0, and 70 .7 percent locations on the
performance-intensity functions. The results
for normal-hearing listeners are illustrated in
panel A and for subjects with hearing impairment in panel B . Three hearing-impaired listeners were unable to complete the test for the
70 .7 percent hard word condition . The maximum output of our test system was set at 110
dB SPL to minimize presentation of speech at
levels that would be uncomfortable for hearingimpaired subjects . During the "up" sequences
required by the adaptive strategy for the 70 .7
percent correct condition, the saturation level of
the equipment was occasionally reached for
these subjects, and the program could not be
completed . The ages of these subjects were 54,
77, and 79 years, and the speech thresholds
were among the highest in the group (38-52
dB) . Inspection of the individual data indicated
that performance was, on average, measured
within the linear portion of the response curves
for the hearing-impaired and the normal-hearing listeners, that is, the decibel difference
between results from 29 .3 to 50 .0 percent are
practically the same as those measured between
50 .0 and 70 .7 percent . We assumed that a similar growth function applied to the aforementioned three subjects and that the test at 70 .7
was incomplete because of limitation in the
intensity range available in the equipment . The
individual difference levels between 29 .3 and
50 .0 percent conditions for each subject were calculated . This difference score was then applied
to the level measured at 50.0 percent to estimate
the result for the 70 .7 percent condition . For
the remainder of the analysis, these three estimated levels were used to form a complete and
more appropriate set of data.
Table 4 shows the means and standard deviations for the experimental conditions for both
normal and hearing-impaired subjects . As anticipated, from earlier experimental results of the
effects of lexical characteristics on word recognition (Lute, 1986 ; Luce and Pisoni, 1998 ; Dirks
et al, 2001), mean performance levels were
always lower for the easy as compared with the
hard words at each experimental condition and
for both groups of subjects . As shown in Table
4, the difference in performance levels between
easy and hard words ranged from 3 .0 to 5.8 dB,
averaging approximately 4.0 dB . Thus, the difference in word recognition for easy versus hard
words is a consistent and robust effect and can
be generalized to listeners with hearing impairment and normal-hearing listeners.
As shown in Figure 1, the performance-intensity functions, estimated from the mean scores at
each condition, are characterized by much steeper
growth functions for normal-hearing listeners
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Journal of the American Academy of Audiology/Volume 12, Number 5, May 2001
teners (as also reflected in the observed difference in slopes of the performance-intensity
functions between the two experimental
groups). Separate 2 (easy, hard words) x 3
(performance conditions : 29 .3, 50 .0, 70 .7%)
ANOVAs were also conducted on data for normal and hearing-impaired listeners. The results
demonstrated a significant difference between
easy and hard word recognition for normal listeners (F = 178 .53, df = 1, 19, p < .001) and for
hearing-impaired listeners (F = 34 .03, df = 1,
19, p < .001), together with a significant difference among the measured levels for the performance conditions of normal (F = 136.8, df =
2, 38, p < .001) and hearing-impaired listeners
(F = 132.26, df = 2, 38, p < .001) . A post hoc
paired contrast analysis showed that the difference between easy and hard words at each
performance level was significantly different for
both normal (F = 30 .69 [70.7%1, 58 .0 [50.0%1,
70 .0 [29.3%1, p < .001) and hearing-impaired listeners (F = 21 .2 [70.7%1, 12 .8 [50 .0%1, 9.94
[29.3%1, p < .005). In a final ANOVA, the results
showed that the mean difference scores (hard
and easy word recognition) at each performance
level were not significantly different between
normal and hearing-impaired subjects, even
though the difference score was always slightly
larger for the hearing-impaired versus the
normal-hearing listeners (see Table 4) .
To examine the effects of age on the ability
to recognize easy and hard words, the results
from the hearing-impaired listeners were subdivided between measured performance levels
for the middle-aged (<60 years) and old (>60
years) subjects . The average age of the middleaged group was 50 years. Only one subject was
younger (23 years of age) than 40 years. The
average age for the older group was 75 .1 years.
The pure-tone and speech audiometric results
from these subgroups have already been
described (see Table 1) . Table 5 shows the results
for easy and hard word recognition for the young
and middle-aged listeners. The middle-aged
0 50 .0
29 .3
Figure 1
Mean word recognition performance for
normal-hearing (A) and hearing-impaired listeners (B) at
three locations (29 .3, 50.0, 70 .7 percent correct) on the performance-intensity function for lexically easy and hard
words.
than for listeners with mild-to-moderate sensorineural hearing loss . For normal-hearing listeners, the slope of the performance-intensity
function (between 70 .7 and 29 .3%) for easy words
was 4.1 percent per dB and for hard words was
3.8 percent per dB . For listeners with hearing
impairment, the slope of the response curve for
easy words was 2.1 percent per dB and for hard
words 1.9 percent per dB . Similar results showing a much reduced growth function for monosyllabic words for individuals with sensorineural
hearing loss versus normal-hearing listeners
have been previously reported (Wilson et al, 1976 ;
Beattie and Warren, 1983 ; Beattie, 1989).
The general observations concerning easy
versus hard word recognition were confirmed
by a 2 x 2 x 3 mixed ANOVA with groups (normal, hearing impaired), lexical category (easy,
hard), and performance conditions (29.3, 50 .0,
70 .7%) as within-subject variables. The results
showed main effects for groups (F = 171.37, df
= 1, 38, p < .001), for lexical category (F =
101.19, df= 1, 38, p < .001), and for performance
condition (F = 246 .1, df = 2, 76, p < .001) . There
was a significant group by performance interaction (F = 26 .44, df = 2, 76, p < .001) owing to
differences in the rate of increase in word recognition for normal and hearing-impaired lisTable 4
Percent Correct Performance Levels (Means and SDs) for Easy and Hard Word Recognition
Among Normal and Hearing-Impaired Listeners at Each Experimental Condition
Normal Hearing
Stimuli
Easy words
Hard words
Difference scores
(hard-easy)
240
70 .7
Hearing Impaired
50.0
29 .3
70 .7
50.0
29.3
35 .3 (3 .8)
39 .3 (3 .6)
29 .4 (3 .4)
33 .1 (3 .6)
25 .2 (3 .8)
28 .4 (3 .5)
72 .7 (13 .3)
78 .5 (14 .2)
62 .8 (10 .3)
67 .0 (11 .5)
53 .4 (9.5)
56 .5 (10 .9)
4 .0 (3 .2)
3 .7 (2 .2)
3 .2 (1 .7)
5 .8 (5 .6)
4 .2 (5 .2)
3 .1 (4 .4)
Effects of Lexical Factors/Dirks et al
Table 5
Mean Performance Levels (Means and SDs) for Easy and Hard Word Recognition
for Middle-Aged (<60 Years) and Old (>60 Years) Hearing-Impaired Listeners
Group/Condition
Middle-aged
70 .7*
50 .0
29 .3
70 .7
Older
Difference
(older-middle-age)
Hard
Easy
50 .0
29 .3
70 .7
50 .0
29 .3
69 .3
59.5
51 .4
76 .0
(12 .3)t
(8 .6)
(6 .9)
(14 .1)
66 .2 (11 .1)
55 .4 (11 .6)
6 .71
6 .7
4.0
Difference (Hard-Easy)
74 .7 (13.3)
63 .2 (10 .4)
54 .2 (8 .4)
83 .2 (15 .8)
5 .4t
3 .7
2 .8
7 .2
70 .7 (11 .7)
4.5
58 .8 (13 .0)
3 .4
8 .5
7 .5
4.6
*Percent correct recognition ; rdB SPL, 'relative dB,
group showed slightly better performance levels than the older group for each easy and hard
word recognition condition . As described in Table
5, the differences in performance between the old
and middle-aged subjects ranged from 4 .0 to
8 .5 (consistently smaller for the middle-aged
group) . The difference in results between the
middle-aged and older subjects is likely related
to the more sensitive average pure-tone and
speech recognition thresholds observed in the
middle-aged group . The spondee thresholds were
3 .1 dB more sensitive for the middle-aged than
the older group . In addition, pure-tone thresholds above 2 .0 kHz were slightly higher for the
older than the middle-aged group .
Of somewhat greater interest in the current investigation were the differences in hard
and easy word recognition for the middle-aged
and older subjects (see Table 5) . Although the difference scores were consistently larger for the
older population than the middle-aged group,
over the three performance conditions, these
differences averaged only 1 .0 dB . To assess these
differences, while adjusting for the difference in
hearing loss between the groups, an analysis of
covariance was conducted between the scores for
the middle-aged and older hearing-impaired
subjects with pure-tone average (average of
thresholds for 0 .5, 1 .0, 2 .0, and 3 .0 kHz) as the
covariate . The results (F = 0 .26, df = 1, 17, p =
.617) indicated that the difference in performance (hard-easy) between the middle-aged and
older subjects was not statistically significant .
Interestingly, the difference scores between hard
and easy word recognition among the normalhearing listeners (see Table 4) were also simi-
lar to those observed in both subgroups of
hearing-impaired listeners (with the possible
exception of the 70 .7, hard word condition for the
older subjects) . There was a tendency for larger
hard-easy difference scores among the older as
compared with the middle-aged hearingimpaired subjects or normal-hearing listeners .
However, statistical analysis of the data for
hearing-impaired subjects did not support the
hypothesis that the effects of lexical difficulty
were disproportionately greater for older than
younger subjects .
DISCUSSION
he purpose of this investigation was to
T examine the relative performance of normal
and hearing-impaired listeners for lexically
easy versus hard word recognition . As predicted
by the NAM theory, the current results verified
earlier findings that both normal-hearing listeners and individuals with acquired hearing
loss were more accurate at recognizing lexically easy than hard words. An earlier investigation (Dirks et al, 2001) from this laboratory,
conducted at fixed presentation levels, indicated that the effects of neighborhood structure and word frequency applied similarly to
normal-hearing and elderly listeners with
acquired hearing loss . The current data demonstrate that the same effects are generalizable
to middle-aged subjects and elderly individuals
with hearing loss . Specifically, the effects of
lexical difficulty favoring easy over hard word
recognition averaged 4.0 dB when performance
was measured at the locations of performanceintensity functions not limited by ceiling or
floor effects. Thus, despite reduced audibility
and other processing difficulties associated with
acquired sensorineural hearing loss, word recognition was influenced by neighborhood structure
and word frequency factors in essentially the
same manner for persons with hearing loss as
for young listeners with normal hearing.
241
Journal of the American Academy of Audiology/Volume 12, Number 5, May 2001
Traditionally, word recognition tests, such
as the NU-6 or CID W-22, have been designed
primarily with the assumption that phonetic or
phonemic discrimination was the critical dimension in the assessment of speech recognition
ability. Without denying the significance of
phonemic discrimination ability, the results from
the current and other experiments on NAM suggest that lexical factors also play an important
role in word recognition. The magnitude of the
effect of lexical factors on word recognition relative to acoustic-phonetic factors is not entirely
clear. For a more comprehensive and valid
assessment of word recognition, however, future
developments of speech materials should include
consideration of higher-level properties (lexical
factors) on "bottom-up" acoustic-phonetic processing. Quantitative values for neighborhood
density and frequency and word frequency are
available, which make it possible to incorporate
these factors in future developments of word
recognition tests.
Earlier findings from Sommers (1996) suggested that neighborhood structure, as defined
by the NAM, had differential effects on recognition in older than younger adults with normal
hearing. Specifically, those results showed an
age-related deficit in the ability to identify hard
words for conditions in which performance
between the older and younger adults was essentially identical for easy word recognition .
Although there was a trend, in the current
study, for larger differences between performance for easy versus hard words for the older
subjects with hearing loss, there was no statistical difference between performance for the
older than for the middle-aged adults with
acquired hearing loss . This result is similar to
earlier findings (Dirks et al, 2001) from this
laboratory, which showed that older subjects
with hearing loss performed in essentially the
same manner on lexically based word lists as
young normal-hearing listeners once speech
presentation level was adjusted for the hearing
loss in the former. Because of differences in
design and subjects, the results from our laboratory and the findings of Sommers (1996) are
not straightforward . Several of these differences are described in detail because the issues
involved impact other related studies in this
investigative area .
First, there are differences in the characterization of easy and hard words, not only
between the current study and Sommers' (1996)
investigation but also more generally among
experiments in which easy versus hard word
242
recognition has been the topic for study. In the
current investigation, easy and hard words
were differentiated on the basis of three lexical
factors : word frequency of the target, neighborhood density, and average neighborhood frequency (easy words had a high word frequency
count with low neighborhood density and frequency, whereas hard words had low word frequency and high neighborhood density and
frequency) . This characterization is the same as
was reported by Toretta (1995) in the development of a multitalker speech database of easy
and hard words and was also used in the investigation of Bradlow and Pisoni (1999) . Sommers, however, emphasized differences in
neighborhood structure, which are the central
factors in the NAM theory, and essentially controlled for word frequency. Although the word
frequency of the target words was not specifically reported in the Sommers' paper, a later
report (Sommers, 1998), in which the same
word sets were used, indicated that the frequency of the easy words was 51 .2, whereas
the frequency of the hard words was 43 .4 (Sommers, 1998). The intent was most likely to control the word frequency factor and vary the
neighborhood structural characteristics of the
easy and hard words . Analysis of results from
Dirks et al (2001) indicated that each of the
three lexical characteristics do not affect word
recognition independently but rather that the
words are recognized relationally, that is, the factors covary. Those results indicated that word
frequency accounts for a larger proportion of the
variance among the experimental word lists
(each containing different combinations of the
lexical factors) than did neighborhood density
and mean neighborhood frequency. Because the
word frequency count for easy and hard words
was controlled in the Sommers (1996) investigation, its effects on easy versus hard word
recognition may not be the same as in the current investigation or that of Bradlow and Pisoni
(1999) . Because of the differences in characterization of easy and hard words among investigators, there are some limitations in comparing
results among the experiments where this classification system has been used . Whether the
differences in the lexical characterization of
easy and hard words among the investigations
are related to the finding of an age-related
deficit in hard word recognition is unclear but
provides an interesting topic for future research .
Second, the populations of the adults who
served as subjects in these studies also differed .
Sommers (1996) compared recognition between
Effects of Lexical Factors/Dirks et al
younger and older adults with essentially normal hearing (however, the older subjects did
have reduced hearing relative to the younger
group at frequencies above 1 .0 kHz) . In the current study, the age-related issue was primarily
assessed by comparing recognition performance
between older and middle-aged adults with hearing losses . Attempts to develop a group of elderly
subjects with normal hearing often require a
wider tolerance of hearing threshold than for a
younger subject population . The presence of a
high-frequency loss among the elderly group
often compromises the audibility of some phonemic elements among these listeners .
A third issue concerns differences in the
phonemic structure of the words used in studies in which easy and hard recognition has been
assessed . In the current study, the phonemic
structure for the easy and hard words was controlled within limits imposed by the small CVC
database available from the corpus of words
that conform to the lexical criteria . Phonemic
control was also imposed for the word sets used
by Sommers (1996) . As indicated previously,
because of differences in the definition of easy
and hard words, the words available and chosen by Sommers as easy and hard were, in general, different than those available for the
current investigation . The words themselves
and their phonemic structure are understandably somewhat different between the two studies . Whether the phonemic structure of the
words interacts with lexical characteristics to
influence intelligibility is not entirely clear,
but, especially when adults with hearing loss are
used as subjects, this issue needs to be
addressed .
In summary, an investigation was conducted to determine the effects of several lexical properties of words on recognition . The
words chosen were divided into two sets of
lexically easy and hard words based on the
NAM theory and word frequency. Adaptive
procedures were used to measure performance
for these word sets at several locations on the
linear portion of the respective performanceintensity functions for normal and hearingimpaired listeners . The findings verified the
NAM predictions, demonstrating that easy
words produce higher recognition performance
than hard words and that the results are generalizable to middle-aged and older adults
with acquired hearing loss . The results indicate that word recognition is influenced by
the lexical properties of words and the more
well-known acoustic-phonemic properties .
Acknowledgment . We thank P Douglas Noffsinger from
the Veterans Administration Greater Los Angeles
Healthcare System and Stephan Fausti, director for
National Center for Rehabilitative Auditory Research,
Veterans Administration Medical Center, for their support and helpful comments on this research . We are
grateful to the audiologists at Veterans Administration
Greater Los Angeles Healthcare System, West Los Angeles
Medical Center, and the UCLAAudiology Clinic for assistance in recruiting subjects with hearing impairment .
We also thank Richard Wilson and Anne Strouse for several helpful suggestions during the review.
This work was supported by grants from the
Department of Veterans Affairs Rehabilitative Research
and Development Service to the National Center for
Rehabilitative Auditory Research (RCTR 597-1060), a
Veterans Affairs Merit Review Award (C2225R), and the
Hope for Hearing Research Foundation.
Portions of this article were presented at the Veterans
Affairs Rehabilitative Research and Development Service
Second Annual Meeting, Crystal City, MD, February 2000 .
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APPENDIX
Stimulus List
Easy Words
boss
cause
chain
cheap
check
church
coach
death
deck
deep
dirt
dive
does
dog
down
feed
firm
food
gang
gap
gas
girl
give
god
hung
join
joke
judge
june
king
learn
leg
live
lodge
long
loud
love
mouth
move
neck
noise
nurse
page
palm
path
peace
pool
pull
put
reach
real
ridge
rob
roof
rough
sang
shape
Hard Words
shirt
shop
soil
tape
teeth
thick
thing
tongue
tough
turn
vain
vote
wash
watch
wife
work
wrong
young
bait
ban
bead
beak
boot
cake
chat
cheer
chill
chore
cod
con
cone
doom
dot
dumb
dune
fade
fake
fin
goat
gore
gut
hash
hick
but
lad
lame
lice
lime
mace
mare
mice
mid
moan
mum
nut
pawn
pin
rum
rut
sane
sill
soak
soar
suck
tan
ear
tile
ton
tune
wad
keen
kin
kit
knit
raid
rat
rhyme
rim
wail
watt
weed
wick
cot
jot
dill
lace
dad
dame
debt
den
pun
roar
wade