J Am Acad Audiol 16:726–739 (2005)
Speech Recognition in Multitalker Babble
Using Digits, Words, and Sentences
Rachel A. McArdle*
Richard H. Wilson†
Christopher A. Burks†
Abstract
The purpose of this mixed model design was to examine recognition performance
differences when measuring speech recognition in multitalker babble on
listeners with normal hearing (n = 36) and listeners with hearing loss (n = 72)
utilizing stimulus of varying linguistic complexity (digits, words, and sentence
materials). All listeners were administered two trials of two lists of each material
in a descending speech-to-babble ratio. For each of the materials, recognition
performances by the listeners with normal hearing were significantly better than
the performances by the listeners with hearing loss. The mean separation
between groups at the 50% point in signal-to-babble ratio on each of the three
materials was ~8 dB. The 50% points for digits were obtained at a significantly
lower signal-to-babble ratio than for sentences or words that were equivalent.
There were no interlist differences between the two lists for the digits and words,
but there was a significant disparity between QuickSIN™ lists for the listeners
with hearing loss. A two-item questionnaire was used to obtain a subjective
measurement of speech recognition, which showed moderate correlations
with objective measures of speech recognition in noise using digits (r = .641),
sentences (r = .572), and words (r = .673).
Key Words: Auditory perception, hearing loss, speech perception, word
recognition in multitalker babble
Abbreviations: ANSI = American National Standards Institute; S/B = signalto-babble ratio; SNR = signal-to-noise ratio
Sumario
El propósito de este diseño de modelo mixto fue examinar diferencias en el
desempeño de reconocimiento, cuando se miden reconocimiento del lenguaje
en medio de balbuceo de hablantes múltiples, en sujetos con audición normal
(n = 36) y sujetos con hipoacusia (n = 72), usando estímulos de complejidad
lingüística variada (dígitos, palabras y frases). Todos los sujetos se sometieron
a dos ensayos de dos listas de cada tipo de material en tasas descendentes
de lenguaje/balbuceo. Para cada uno de los materiales, el desempeño de
reconocimiento por parte de los sujetos normo-oyentes fue significativamente
mejor que el desempeño de aquellos con hipoacusia. La separación media
entre los grupos en el punto del 50% de las tasas señal/balbuceo en cada uno
de los tres tipos de materiales fue ~8 dB. El 50% de los puntos para dígitos
se obtuvo a una tasa señal/balbuceo significativamente menor que para frases
o palabras que eran equivalentes. No existieron diferencias inter-listas entre
*Bay Pines VA Healthcare System, Bay Pines, Florida; †James H. Quillen VA Medical Center, Mountain Home, Tennessee
Rachel A. McArdle, Ph.D., Bay Pines VA Healthcare System, Audiology (126), P.O. Box 5005, Bay Pines, FL 33744; Phone:
727-398-9395; Fax: 727-319-1209; E-mail: [email protected]
This work was supported by the Rehabilitation Research and Development Service, Department of Veterans Affairs through
a Research Career Development award to the senior author, a Senior Research Career Scientist award to the second author, and
a Research Enhancement Award Program (REAP) to Mountain Home.
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Recognition of Digits, Words, and Sentences in Babble/McArdle et al
las dos listas de dígitos y palabras, pero hubo una disparidad significativa entre
las listas de QuickSIN™ para los sujetos con hipoacusia. Se utilizó un
cuestionario de dos elementos para obtener una medición subjetiva del
reconocimiento del lenguaje, que mostrara una correlación moderada con
medidas objetivas de reconocimiento del lenguaje en ruido usando dígitos (r
= .641), frases (r = .572) y palabras (r = .673).
Palabras Clave: Percepción auditiva, hipoacusia, percepción del lenguaje,
reconocimiento del lenguaje en medio de balbuceo de hablantes múltiples
Abreviaturas: ANSI = Instituto Nacional Americano de Estándares; S/B = tasa
señal/balbuceo; SNR = tasa señal/ruido
T
he most universal complaint reported
by older individuals with hearing loss
is difficulty understanding speech in
the presence of background noise (Carhart
and Tillman, 1970; Dubno et al, 1984; van
Rooij and Plomp, 1990, 1992; Bronkhorst,
2000; Horwitz et al, 2002; Killion, 2002;
Wilson and Strouse, 2002), yet measurement
of speech-recognition abilities in quiet
continues to be standard practice within most
audiology clinics. A recent survey on
audiological practices showed that of the 91%
of audiologists who responded that they
routinely administer word-recognition tests,
92% administer suprathreshold monosyllabic
word lists in quiet (Martin et al, 1998). A
limitation of word-recognition testing in a
quiet background at one presentation level is
that the results are not representative of
how an individual will perform in real-world
situations (e.g., noisy) under amplification
(Dirks et al, 1982; Plomp, 1986; CHABA,
1988). Speech-recognition testing in quiet
also does not address the chief complaint of
the majority of patients with hearing loss,
which is difficulty understanding speech in
noise.
Numerous studies show that pure-tone
audiograms and speech-recognition scores
in quiet do not predict the ability of an
individual with hearing loss to understand
speech in noise (Cherry, 1953; Groen, 1969;
Carhart and Tillman, 1970; Plomp, 1978;
Plomp and Mimpen, 1979; Dirks et al, 1982;
Beattie, 1989; Killion and Niquette, 2000;
Wilson, 2003). The inability to predict wordrecognition performance in noise scores can
be explained using the Plomp (1978) twofactor framework of hearing loss that suggests
both attenuation and distortion are involved
with decreased hearing function.
Pure-tone thresholds and, to some degree,
the ability to recognize speech in a quiet
background are measures of audibility or
lack of audibility that Plomp (1978) refers to
as attenuation. The ability to recognize speech
in a background noise is a different aspect of
auditory behavior that involves a distortion
factor. Regardless of whether the distortion
is peripheral, such as increased bandwidths
that lead to poorer frequency resolution, or
whether the distortion is a result of agerelated changes in the central nervous
system, a main indication of distortion is a
decreased ability of the listener to understand
degraded speech stimuli such as speech in
background noise. In terms of rehabilitation,
hearing aids are used to make speech audible,
which remediates the attenuation factor for
most individuals. Individuals who also have
a distortion component to their hearing loss
often demonstrate less benefit from
amplification and require extensive
counseling on realistic expectations for
hearing-aid use.
Because of the distortion component of
hearing loss (Plomp, 1978), the ability to
recognize speech in noise cannot be predicted,
and it must be measured directly (Killion,
2002). Incorporating background noise into
standardized speech tests improves the
sensitivity and validity of word-recognition
measures (Findlay, 1976; Beattie, 1989;
Willott, 1991; Sperry et al, 1997; Wiley and
Page, 1997). Past studies have shown a clear
separation in performance between
individuals with normal hearing and
individuals with hearing loss when measuring
speech recognition in multitalker babble
(Dubno et al, 1984, Beattie, 1989; Wilson and
Strouse, 2002; Wilson, 2003). On average,
individuals with hearing loss have required
the signal to be 10–12 dB higher than the
multitalker babble to obtain a performance
level of 50% correct, whereas individuals
with normal hearing reach 50% correct at
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Journal of the American Academy of Audiology/Volume 16, Number 9, 2005
signal-to-babble ratios (S/B) of 2–6 dB. Thus,
it appears that not only do individuals with
impaired hearing have a pure-tone sensitivity
loss, but also a signal-to-noise ratio (SNR) loss
that is unpredictable from objective measures
in quiet. In addition, subjective reports of
communication ability in a noisy environment
have failed to show any relationship between
how a patient perceives his or her ability to
recognize speech in a noisy environment and
objective measures of speech recognition in
multitalker babble (Rowland, et al, 1985;
Wilson et al, in press).
Information gained from measuring
speech recognition in noise is useful not only
in differentiating between normal and
impaired hearing but for rehabilitation
purposes. Measuring SNR loss is also useful
for selecting amplification strategies (e.g.,
directional microphones or FM systems),
corroborating a patient’s claim of activity
limitations, and counseling concerning
expectations for hearing-aid performance.
Audiologists, however, have not embraced
speech-in-noise testing as a routine part of
their clinical procedures. Reasons postulated
include: (1) past practice stressing speech
audiometry in quiet instead of a more
ecologically valid approach and (2)
audiologists being unaware of the
rehabilitative usefulness provided by the
speech-in-noise data (Wilson and Strouse,
2002). Additionally, many audiologists are
unfamiliar with interpreting SNR loss since
it is often measured in dB S/B versus the
more clinically historic metric of percent
correct.
The importance of objectively assessing
speech in noise in the adult population led to
the recent development of standardized
speech tests using sentence materials that
include (1) the Connected Speech test (CST;
Cox et al, 1987), (2) the Hearing In Noise
Test (HINT; Nilsson et al, 1994), (3) the
Speech In Noise Test (SIN™; Etymotic
Research, 1993), and (4) the QuickSIN
(Etymotic Research, 2001; Killion et al, 2004).
An advantage to sentence-length stimuli is
the ability to score multiple target words in
a short amount of time. For example, one
test list in the QuickSIN includes 30 target
words arranged into six sentences that can
be administered in less than a minute and
provide a measurement of SNR loss. On the
other hand, a disadvantage to sentence-length
stimuli, especially in an older population, is
that repeating sentence materials, especially
728
in background noise, involves cognitive skills
beyond a simple one-word, speech-recognition
task. The additional cognitive demands may
have a differential effect, particularly on
older listeners (Salthouse, 1985; Craik, 1994).
Sentence-length stimuli require an individual
to recall multiple words from working
memory. Recall of multiple words can be
influenced by recency and primacy effects
(Murdock, 1962), such that the first and last
words of a string of words are easier to recall.
Also, the syntactic and semantic structure of
sentence-length stimuli influences
performance such that it is easier to recall
multiple words that follow grammar rules and
are meaningful (Wingfield, 1996). By
presenting sentence-length stimuli in
competing noise, the ability to use
compensatory strategies for remembering
strings of words such as rehearsal strategies
(mental repetition of information to be
recalled) and elaborative encoding (linking of
new information to knowledge already stored
in long-term memory) may be inaccessible
(Craik and Lockhart, 1972; Rabbitt, 1990).
Recently, monosyllabic word and digit
materials in multitalker babble were shown
to be sensitive to the different recognition
abilities of listeners with normal hearing
and listeners with hearing loss (Wilson et
al, 2003; Wilson and Weakley, 2004; Wilson,
Burks, et al, forthcoming). Although the use
of monosyllables, of which monosyllabic digits
are a special case, as test stimuli in speechtesting paradigms has been criticized for
lacking natural dynamics of real speech such
as word stress, co-articulation, and dynamic
range (Villchur, 1982), words remain the most
popular stimulus type among audiologists
and minimize the effects on performance of
working memory and linguistic context.
This study was designed to examine the
differences among speech-in-noise tasks using
stimuli with varied linguistic context. Digits,
words, and sentences in multitalker babble
were used to examine the S/B needed by
listeners with normal hearing and hearing
loss to reach a criterion of 50% correct on a
closed set (digits), an open set with syntactic
context (QuickSIN), and an open set without
context (monosyllables). It was hypothesized
that performance would follow the same
continuum such that, as the amount of
linguistic context increased from digits to
sentences to words, the need for an improved
S/B would also increase to reach 50% correct.
Furthermore, our interest was to again
Recognition of Digits, Words, and Sentences in Babble/McArdle et al
examine the relationship between a subjective
rating of performance in noise with objective
measurements of SNR loss.
Research, 2001; Killion et al, 2004), (2)
monosyllabic words in multitalker babble
(Wilson, 2003), (3) digit triplets in multitalker
babble (Wilson and Weakley, 2004), and (4)
Northwestern University Auditory Test No.
6 (NU No. 6; Tillman and Carhart, 1966) in
quiet. The presentation paradigms and the
signal-to-noise ratios of each material were
not altered from their original protocols.
Each QuickSIN list consisted of six IEEE
sentences, each with five target words. The
level of the sentences was fixed and the level
of the multitalker babble (Auditec of St.
Louis), which is continuous throughout the
list of sentences, was varied in 5 dB
increments from 25 to 0 dB S/B. The sentences
were 2.5 to 3.0 sec with a 5 to 6 sec interval
between sentences, during which time
responses were made and recorded. Each list
of sentences was ~55 sec. List 3 and List 4 of
the QuickSIN arbitrarily were selected for the
current experiment. The materials were taken
from the commercial version of the QuickSIN
(Etymotic Research, 2001).
The purpose of developing the words-innoise (WIN) paradigm was to provide a
protocol that could be used to evaluate the
abilities of listeners to understand speech in
a background noise. Multitalker babble was
selected as the background noise because it
is probably the most commonly occurring
noise that interferes with communication
(Plomp, 1978). The multitalker babble, which
was also used with the digit materials, was
recorded by D. Causey (pers. comm., 1979) and
consisted of three females and three males
talking simultaneously about different topics
(Sperry et al, 1997). Because five-second
segments of babble were compiled randomly,
the babble was not intelligible. The
word/babble segments were edited at the
negative going zero crossings, which avoided
clicks at the segment boundaries when the
segments were concatenated.
METHODS
T
he experimental design for this study
formed a 2 x 3 x 2 mixed model factorial.
Listener group (normal or impaired hearing)
was varied between subjects, and stimulus
type (digits, sentences, words) and list effects
(3, 4) were varied within subjects. Two lists
of each stimulus type were used to examine
interlist reliability. In addition, test-retest
was addressed.
Participants
Thirty-six young listeners with normal
hearing (18–28 years, mean age = 23.3 years)
and 72 older listeners with sensorineural
hearing loss (31 to 84 years, mean age = 65.5
years) were studied. The younger listeners
were recruited from local universities and
had normal hearing (≤20 dB HL, American
National Standards Institute [ANSI], 1996)
at the 250–8000 Hz octave intervals. Inclusion
criteria for the older listeners, who had
audiometric test results that were consistent
with sensorineural hearing loss, included the
following: (1) >30 years of age, (2) a threshold
at 500 Hz of ≤30 dB HL, (3) a threshold at
1000 Hz of ≤40 dB HL, and (4) a pure-tone
average at 500, 1000, and 2000 Hz of ≤45 dB
HL. The mean pure-tone, air-conduction
thresholds for the test ear (and standard
deviations) are listed in Table 1.
MATERIALS
T
he following four speech-recognition
materials were studied: (1) sentences in
multitalker babble (QuickSIN; Etymotic
Table 1. The Mean (and standard deviations) Ages (years) and Air-Conduction, Pure-Tone Thresholds
for the Test Ear (dB HL; ANSI, 1996) for the 36 Listeners with Normal Hearing and the 72 Listeners with
Hearing Loss
Listeners with Normal Hearing
Mean
SD
Listeners with Hearing Loss
Mean
SD
Frequency in Hertz
1000
2000
Age
250
500
23.3
2.7
5.3
5.6
4.9
5.0
4.6
5.8
65.5
10.5
19.4
8.3
19.7
6.7
21.9
8.4
3000
4000
8000
2.8
6.4
4.4
5.8
2.6
6.4
5.8
8.6
40.4
16.4
56.4
15.0
62.3
16.0
62.4
19.2
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Journal of the American Academy of Audiology/Volume 16, Number 9, 2005
The WIN protocol evolved with the
following characteristics (Wilson, 2003; Wilson
et al, 2003; Wilson et al, 2005): (1) 70
monosyllabic words from NU No. 6 spoken by
the female speaker on the VA compact disc,
which enabled the evaluation of recognition
performance in quiet and in babble with the
same materials spoken by the same speaker;
(2) ten unique words presented at each of
seven signal-to-babble ratios from 24 dB S/B
to 0 dB S/B in 4 dB decrements; (3) each
word was time locked to a unique segment of
babble, which reduced variability; (4) the
level of the babble, which was presented
continuously, was fixed and the level of the
words varied; (5) the interval between words
was 2.7 sec, (6) the 50% point was quantifiable
with the Spearman-Kärber equation (Finney,
1952); (7) a stopping rule that terminated
the test sequence when ten words at one
level were incorrect; and (8) the test provided
a 6 to 12+ dB separation in terms of signalto-babble ratio between performance by
listeners with normal hearing and
performance by listeners with hearing loss.
Subsequently, to reduce the test time so the
instrument would be acceptable for clinic
use, the 70-word protocol was divided into two
35-word equivalent lists based on the error
analysis of the recognition performances of
573 listeners with sensorineural hearing loss
(Wilson, Weakley, et al, forthcoming). Four
randomizations of each list were generated
with List 1 designated as the even-numbered
lists and List 2 designated as the oddnumbered lists. As with the QuickSIN, Lists
3 and 4 of the WIN were selected for the
study.
The digits-in-noise protocol was
developed as a potential screening protocol
following the model suggested by Smits et al
(2004). Digits 1 through 10, excluding
bisyllabic 7, spoken by a male, were grouped
randomly by triplets so that each of the nine
digits was presented in quiet and in
multitalker babble at each of seven signal-tobabble ratios (S/B) that ranged from 4 to -20
dB in 4 dB decrements. The quiet condition
served to acquaint the listener with the
listening task and was not intended for
analysis. The level of the multitalker babble
was fixed, and the level of the digits varied.
The digits ranged from 365 msec (5) to 560
msec (9). Each digit was paired with and
time locked to a unique segment of
multitalker babble with 300 msec of babble
preceding and succeeding the digit. The
730
duration of each digit/babble segment was the
duration of the digit plus 600 msec. The digit
triplets were formed by concatenating the
various digit/babble segments with 300 msec
segments of babble added before the first
digit and after the third digit. These 300
msec segments were shaped with 25 msec
rise/fall times, respectively. Again, the editing
technique at the segment boundaries
produced seamless transitions between
boundaries that were not audible or
perceptually apparent. The ~3.5 sec digit
triplets were separated by a 4 sec quiet
interstimulus interval (ISI), during which
time the responses were made and recorded.
Each of the two lists of the 72 digits recorded
was ~3 min. Lists 3 and 4 of the digit triplets
were used (Wilson, Burks, et al, forthcoming).
For the quiet condition, List 4 of NU No.
6 was divided into two 25-word lists. The
same female speaker who spoke the words for
the multitalker babble paradigm was used
(Department of Veterans Affairs, 1998). Each
list of words was ~2 min. The NU No. 6 lists,
the WIN lists, and the digits-in-noise lists
were recorded on an audio compact disc
(Hewlett Packard, Model DVD200i).
Procedures
Following the pure-tone audiogram, each
listener was asked to respond to two questions
on a scale of 1 to 10 (Wilson et al, 2005).
First, when listening to a conversation in
quiet without your hearing aids, how difficult
is it for you to understand what the speaker
is saying? Second, when listening to a
conversation in a noisy background without
your hearing aids, how difficult is it for you
to understand what the speaker is saying? On
the response scale, “1” was no difficulty
understanding a speaker, and “10” was
extreme difficulty understanding a speaker.
Then, two trials were conducted with
each of the three speech materials presented
monaurally in multitalker babble during a 30minute session. The right ear was tested for
the odd-numbered listeners, and the left ear
was tested for the even-numbered listeners.
With the QuickSIN, the level of the words was
fixed at 90 dB SPL, and the level of the babble
varied from 25 to 0 dB S/B (65 dB SPL to 90
dB SPL) in 5 dB steps. For each list of digits,
three digit triplets (i.e., nine digits) were
presented in quiet at 80 dB SPL to acquaint
the listener with the task. Then the level of
the babble was fixed at 80 dB SPL, and the
Recognition of Digits, Words, and Sentences in Babble/McArdle et al
level of the digits varied from 4 to -20 dB S/B
(84 dB SPL to 60 dB SPL) in 4 dB steps.
With the words in babble, the level of the
babble was fixed at 80 dB SPL, and the level
of the words varied from 24 to 0 dB S/B (104
dB SPL to 80 dB SPL) in 4 dB steps. Thus,
the QuickSIN maintains the level of the
speech and varies the level of the babble,
whereas the WIN and digits-in-noise
maintain the level of the babble and vary
the level of the speech. These paradigms were
maintained in this experiment because the
comparisons were among the three protocols
as each was intended for use in the clinic. A
near common condition between the
QuickSIN and words in babble was when
the QuickSIN was presented at 10 dB S/B
(speech at 90 dB SPL and babble at 80 dB
SPL), and when the words in babble were
presented at 8 and 12 dB S/B (speech at 88
and 92 dB SPL and babble at 80 dB SPL).
The word and sentence materials in
multitalker babble were reproduced on a
compact disc player (Sony, Model CDP-497),
routed through an audiometer (GrasonStadler, Model 10) to a TDH-50P earphone
encased in a Telephonics P/N 510C017-1
cushion. The non-test ear was covered with
a dummy earphone. On the even-numbered
listeners, the right ears were used, with the
left ears used on the odd-numbered listeners.
All testing was conducted in a double-wall
sound booth with the verbal responses of the
listeners recorded into a spreadsheet.
A counterbalanced design was used in
which (1) Trial 1 on each material was
completed before Trial 2 was conducted, i.e.,
all three materials were presented once before
the second presentation of any of the
materials; (2) the two lists of each material
were presented an equal number of times in
Trial 1 and Trial 2; and (3) each of the three
materials was presented an equal number of
times in the six possible orders. Following the
Figure 1. The psychometric functions of the mean data for List 3 (open symbols) and List 4 (filled symbols) of
the QuickSIN, digits, and word materials are shown for the listeners with normal hearing (squares) and for the
listeners with hearing loss (circles). Quadrant IV depicts the mean functions for Lists 4 of the three materials.
The key for the fourth panel is shown in the lower right corner with the data from the listeners with normal
hearing depicted as open symbols and the data for the listeners with hearing loss depicted as filled symbols.
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Journal of the American Academy of Audiology/Volume 16, Number 9, 2005
Table 2. The Mean 50% Correct Points (dB S/B) and Standard Deviations (dB) Established with the
Spearman-Kärber Equation (Finney, 1952) for the Three Materials and the Two Subject Groups.
Mean
Normal Hearing
SD
Slope
Mean
SD
Hearing Loss
Slope
Difference
QuickSIN
List 3
List 4
4.3
3.9
2.1
2.0
15.8
14.0
13.3
10.1
5.0
4.8
4.7
6.6
9.0
6.2
Digits
List 3
List 4
-11.9
-11.8
2.6
3.0
6.5
6.5
-4.0
-3.9
3.8
3.4
6.3
6.6
7.9
7.9
Words
List 3
List 4
5.0
4.4
2.4
2.0
5.8
6.7
12.4
12.3
3.5
3.8
6.9
7.2
7.4
7.9
Note: The slopes of the mean functions (%/dB) depicted in Figure 1 also are listed.
six babble conditions, two 25-word lists
compiled from List 4, NU No. 6, were
presented in quiet. Half of the listeners were
presented the first 25 words at 60 dB HL, and
the second 25 words at 80 dB HL. The order
was reversed for the remaining listeners.
These two levels corresponded to the levels
of the WIN words near the extremes of the
function and ensured that audibility was not
an issue at the less favorable signal-to-babble
ratios.
RESULTS
T
he psychometric functions for the two
lists of each of the three speechrecognition materials are shown in Figure 1.
The mean data for the listeners with normal
hearing (squares) and for the listeners with
hearing loss (circles) are shown for List 3
(open symbols) and List 4 (filled symbols) of
the respective materials in the upper and
left panels. The data in the lower-right panel
depict the mean functions for both listener
groups on List 4 of each the three materials
along with the key to the panel. The lines
through the datum points are the best-fit,
third-degree polynomials used to describe
the data. In addition to the mean data at
each presentation level, the 50% points on the
individual functions were quantified with
the Spearman-Kärber equation (Finney,
1952). The mean 50% points (in dB S/B) and
standard deviations (dB) are listed in Table
2 along with the slopes at the 50% points
(%/dB) of the mean functions shown in
Figure 1.
The individual 50% points established
with the Spearman-Kärber equation were
subjected to a repeated-measures analysis
732
of variance (ANOVA) with one betweensubjects variable, listener group (normal
hearing and hearing loss), and two withinsubjects variables, stimulus type (QuickSIN,
digits, and words) and list (Lists 3 and 4). As
might be expected from the data in Figure 1
and Table 2, the main effects of listener group
(F[1,106] = 99.31, p < .001) and stimulus
type (F[2,212] = 1875.52, p < .001) were
statistically significant. Not as expected was
the significant difference between lists
(F[1,106] = 13.49, p < .001) with the 50%
point for List 3 0.8 dB lower than the 50%
point for List 4. In addition to the significant
main effects of stimulus type and list, the
interaction of the two effects also was
statistically significant (F[2,212] = 11.35, p
< .001) as well as the three-way interaction
between listener group, stimulus type, and list
(F[2,212] = 8.43, p < .001). Post hoc t-tests
with Bonferroni corrections for multiple
comparisons revealed that for the interaction
between stimulus type and list, the only
significant difference between Lists 3 and 4
was for the QuickSIN materials. Also, for the
three-way interaction, post hoc t-tests with
Bonferroni corrections for multiple
comparisons revealed that the significant
difference in list for the QuickSIN was only
seen for listeners with hearing loss. No other
post hoc comparisons for this three-way
interaction were statistically significant.
Examination of the mean data in Figure
1 and Table 2 provides a clear picture of the
significant main effects from the ANOVA.
First, for listener group, each of the three
materials provided a significant difference
between the performances by listeners with
normal hearing and listeners with hearing
loss. For equal recognition performance, the
Recognition of Digits, Words, and Sentences in Babble/McArdle et al
listeners with hearing loss required an ~8 dB
more favorable signal-to-babble ratio than
did the listeners with normal hearing. The ~8
dB difference between the data from the
listeners with normal hearing and the
listeners with hearing loss was consistent
among all stimulus types, which is supported
by a lack of an interaction in the ANOVA
between listener group and stimulus type. For
the digit and word materials, this ~8 dB
difference was consistent throughout the
range of signal-to-babble ratios. The
differences between the two QuickSIN lists
were less systematic and are considered in
detail in the discussion section.
Second, to examine further the main
effect of stimulus type, post hoc t-tests with
Bonferroni corrections for multiple
comparisons were performed. The results
revealed that across listener groups the 50%
points for the words and QuickSIN were not
significantly different (see Table 2). The 50%
points for the digits, however, were at signalto-babble ratios 16–17 dB below the 50%
points for the words and QuickSIN, which was
a significant difference (p < .001).
The data illustrated in Figure 2 provide
a visualization of all the individual 50%
points involved in the three-way interaction
observed in the ANOVA. The three-panel
bivariate plot shows the 50% point on List 3
(abscissa) and the 50% point on List 4
(ordinate) for the listeners with normal
hearing (squares) and the listeners with
hearing loss (circles). The large filled symbols
are the means for each group. The diagonal
line in each panel represents equal
performance on the two lists with the
numbers in parentheses indicating the
number of datum points above, on, and below
the diagonal. There are two sets of numbers
in parentheses in each panel. The upper
numbers refer to the data from the listeners
with hearing loss (circles), and the lower
numbers refer to the data from the listeners
with normal hearing (squares). Datum points
below the diagonal indicate poorer
performance on List 3 than on List 4. The
solid line represents the best-fit linear
regressions for the listeners with hearing
loss whereas the dotted line represents the
best-fit linear regressions for the listeners
with normal hearing. With each of the three
speech materials, the datum points from the
listeners with normal hearing are equally
distributed around the diagonal line
indicating equal performance on Lists 3 and
Figure 2. The 50% correct points calculated with the
Spearman-Kärber equation for Lists 3 (abscissa) and
Lists 4 (ordinate) for the QuickSIN, digits, and words.
The circles depict the data for the listeners with hearing loss, and the squares depict the data for the listeners with normal hearing. The large filled symbols
illustrate the mean data. The numbers in parentheses indicate the number of datum points above, on, or
below the diagonal line that depicts equal performance.
The thick dashed and solid lines represent linear
regressions used to fit the data.
733
Journal of the American Academy of Audiology/Volume 16, Number 9, 2005
Figure 3. The 50% correct points calculated with the
Spearman-Kärber equation for Trial 1 (abscissa) and
Trial 2 (ordinate) for the QuickSIN, digits, and words.
The circles depict the data for the listeners with
hearing loss, and the squares depict the data for the
listeners with normal hearing. The large filled symbols illustrate the mean data. The numbers in parentheses indicate the number of datum points above, on,
or below the diagonal line that depicts equal performance. The thick dashed and solid lines represent linear regressions used to fit the data.
734
4. The data for the listeners with hearing
loss are not as clear cut. Consider the data for
the QuickSIN (upper panel) in which 55 of the
72 listeners with hearing loss had poorer
performance on List 3 than on List 4, which
was reflected in the significant mean 3.2 dB
difference between the 50% points of the two
lists (Table 2). The digit data (middle panel)
and word data (lower panel) indicate that
about half the listeners in both groups
performed better on List 3 than on List 4
with the other half demonstrating just the
opposite. As the ANOVA three-way interaction
indicated, Lists 3 and 4 of the digit and word
materials produced equivalent results for
the listeners with hearing loss, whereas Lists
3 and 4 for the QuickSIN did not produce
equivalent results.
To address test-retest, the data for the
individual performances on Trial 1 versus
Trial 2 for each stimulus type are shown in
Figure 3. The format of Figure 3 is identical
to the format of Figure 2 except Trial 1
(abscissa) and Trial 2 (ordinate) are the
variables. For the QuickSIN and digit
materials, more datum points are below the
diagonal line (equal performance) than above
the line, with no practical difference between
the trials on the words. This relation indicates
that recognition performance on Trial 1 was
poorer than on Trial 2, suggesting a modest
practice effect with the QuickSIN and digit
materials. The mean signal-to-babble ratios
at the 50% point collapsed across the two
listener groups, and the three stimulus types
were 3.2 dB for Trial 1 and 2.5 dB for Trial
2. The main effect of trial was statistically
significant (F[1,106] = 14.27, p < .001) when
the data were subjected to a repeatedmeasures ANOVA with one between-groups
variable (listener group) and two withingroups variables (stimulus type and trial).
Although the main effect of listener group
and stimulus type remained statistically
significant in this analysis, the interactions
between trial and listener group as well as the
interactions between trial and stimulus type
were not statistically significant; this suggests
that there was a practice effect on performance
for all three stimulus materials. It is important
to note, however, that the 0.7 dB difference
between the mean performances on Trial 1
(3.2 dB S/B) and Trial 2 (2.5 dB S/B) is a
magnitude that for clinical purposes is not
particularly noteworthy.
Speech-recognition testing is often
criticized for an inability to predict real-world
Recognition of Digits, Words, and Sentences in Babble/McArdle et al
Table 3. Spearman rho Correlation Coefficients and Associated p-Value Collapsed across Hearing
Status Groups
Subjective Quiet Ratings
NU No. 6
NU No. 6
(60 dB HL)
(80 dB HL)
Digits
.469
<.001
.641
<.001
-.440
<.001
performance. In the current study, all
participants were asked to rate their ability to
understand speech in quiet and in noise on a
scale of 1 to 10, with 1 representing no difficulty
and 10 representing extreme difficulty. As
expected, listeners with hearing loss reported
higher medians in both conditions (3 = quiet,
8 = noise) than listeners with normal hearing
(1 = quiet, 3 = noise). There was also more
variability in responses for the listeners with
hearing loss as indicated by the full range of
responses for quiet and noise (1–10) as
compared to the responses by listeners with
normal hearing for quiet (1–5) and noise (1–7).
Because the subjective scaling data were
ordinal, the nonparametric correlational
technique, Spearman rho, was used to examine
the relationship between the subjective reports
and objective measurement of speech
recognition in both quiet and noise.
The self-rating of speech understanding
in quiet was correlated with performance on
the NU No. 6 words at 60 (-0.469) and 80 dB
HL (-0.440). The negative correlation
coefficients and associated p-values reported
in Table 3 suggest that the relationship
between the subjective measures of speechrecognition performance in quiet and objective
measures of speech recognition in quiet at
low- and high-presentation levels was
moderate. As expected, low subjective ratings
were correlated with high percent correct
performance for objective speech-recognition
measures in quiet. A separate analysis was
completed for subjective ratings of speech
recognition in noise and the objective
measures of speech recognition in noise
utilizing each of the three stimulus materials.
Performance on List 4 of each stimulus type
was used because of the unsystematic
performance for listeners with hearing loss
on List 3 of the QuickSIN materials. The
correlation coefficients and associated pvalues reported in Table 3 suggest that the
relationship between subjective measures of
speech-recognition performance in noise and
objective performance for each of the three
materials was moderate. Higher subjective
Subjective Noise Ratings
QuickSIN
Words
.572
<.001
.673
<.001
ratings, indicating greater difficulty
communicating in noise, were obtained for
individuals with higher 50% points.
The mean correct recognitions for the
listeners with hearing loss on the NU No. 6
presented in quiet at 60 and 80 dB SPL were
84.3% and 86.3%, respectively, which was a
nonsignificant difference. The recognition
performances at these two presentation levels,
which corresponded to the 0 and 20 dB S/B
conditions with the WIN, ensured that
Figure 4. Bivariate plots of the word-recognition performance
in quiet (ordinate) versus the signal-to-noise ratio at which the
50% point was obtained in multitalker babble (abscissa) with
the QuickSIN (top) and with the words-in-babble (bottom). For
graphic clarity, a jittered algorithm was used that randomly
multiplied the x and y values by 1.025 to 0.975 in 0.05 steps.
The shaded region indicates the area of normal performance
for the two variables. The large filled symbols illustrate the
mean data. The numbers in parentheses are the number of listeners in each quadrant of the plot.
735
Journal of the American Academy of Audiology/Volume 16, Number 9, 2005
audibility was not an issue at either of the
signal-to-babble levels.
Finally, it was of interest to examine the
relationship
between
recognition
performances on List 4 of the NU No. 6 in
quiet at 80 dB HL and on the words in babble
and the QuickSIN. These comparisons are
made in Figure 4, in which correct recognition
in quiet is on the ordinate and the 50% correct
point (in dB S/B) is on the abscissa. The
shaded region in the upper left of each panel
represents performance in the 90th percentile
by listeners with normal hearing for both
the words in noise and the QuickSIN as well
as clinically accepted “good” performance on
recognition performance in quiet. The filled
symbols represent the mean data for listeners
with normal hearing (squares) and the
listeners with hearing loss (circles). The
numbers in parentheses are the number of
listeners in each quadrant of the plot. For
example, in the upper panel, the “46” indicates
the number of listeners who had good word
recognition in quiet (i.e., ≥80%) but were out
of the normal range on the QuickSIN. The
data in both panels indicate that most of the
listeners with hearing loss performed within
the normal range on the NU No. 6 in quiet
but performed outside of the normal range on
both the QuickSIN and the words in babble.
These findings indicate the difficulty
encountered when trying to predict wordrecognition performance in noise from word
recognition in quiet, except when word
recognition in quiet is poor.
GENERAL DISCUSSION AND
CONCLUSIONS
T
he purpose of this study was to examine
the objective performance along with the
subjective rating of listeners with hearing
loss as compared to listeners with normal
hearing on speech-in-noise tasks using stimuli
with varied linguistic context. The use of
digits, words, and sentences in multitalker
babble allowed for a systematic examination
of performance ability from a closed set
(digits), to an open set with syntactic context
(QuickSIN), to an open set without context
(monosyllables). For objective measures, mean
group performances were measured in
decibels signal-to-babble ratio at the 50%
point of the psychometric function, whereas
subjective ratings were obtained using a tenpoint rating scale.
As expected, the young listeners with
736
normal hearing performed ~8 dB better on the
three speech materials than did the listeners
with hearing loss. This represents an ~8 dB
hearing loss in terms of signal-to-noise ratio.
This result is in agreement with previous
reports that examined the separation in
performance between listeners with normal
hearing and hearing loss on a speech-inbabble task (Dubno et al, 1984; Beattie, 1989,
Wilson and Strouse, 2002; Wilson, 2003;
Killion, 2004). In addition, the ~8 dB SNR loss
quantifies the complaint by older listeners
with hearing loss who report difficulty
communicating in noisy environments.
The results for words and digits are
consistent with the findings from previous
studies in terms of performance levels on
speech-in-noise tasks by listeners with hearing
loss. In the current study, words-in-babble
data collected for the listeners with hearing
loss had a 12.4 dB S/B mean at the 50% point
(Table 2) collapsed across lists. This is in
agreement with Wilson and Weakley (2004,
table 6), who reported a mean 50% point at
12.2 dB S/B for a group of 48 listeners with
hearing loss (mean age = 63.5) using twice as
many words at each signal-to-babble ratio
than the current study. With the digit stimuli,
a -6.0 dB S/B at the 50% point was observed
by Wilson and Weakley (2004) that is in close
agreement with the -4.0 dB S/B at the 50%
point in the current study.
Walden and Walden (2004) examined the
recognition performance on the QuickSIN by
listeners with hearing loss and observed a
mean 50% point of 8.3 dB S/B,1 which is 3.4
dB below the mean 50% point measured in the
current study (11.7 dB S/B). Lists 3 and 4 of
the QuickSIN were used in the current study,
whereas Walden and Walden used Lists 5
and 6. The current study showed a significant
effect of list for the listeners with hearing
loss, reflecting a lack of interlist equivalency
for the QuickSIN, which may have contributed
to the discrepancy between studies.
Another interesting result regarding the
QuickSIN was the three-way interaction
between listener group, stimulus type, and
list. Inspection of the psychometric functions
in the top right panel of Figure 1 shows that
for listeners with hearing loss the data for
List 3 of the QuickSIN (dashed line) was
irregular compared to the List 4 data. As the
signal-to-babble ratio is improved, the expected
outcome is improved recognition performance.
The data from List 3, however, did not reflect
this relation. On List 3, recognition
Recognition of Digits, Words, and Sentences in Babble/McArdle et al
performance at the 10 and 20 dB S/Bs was
poorer than at the adjacent lower signal-tobabble ratios (5 and 15 dB S/B, respectively).
The 10 and 20 dB S/B datum points on List 3
also attained substantially lower performances
than were obtained from the corresponding
levels on List 4. The irregularities observed
with List 3 are accountable for the 3.2 dB
difference between the 50% points for List 3
(13.3 dB S/B) and List 4 (10.1 dB S/B) for the
listeners with hearing loss.
A similar, but smaller, discrepancy was
observed in the QuickSIN List 3 data for the
listeners with normal hearing. The 10 dB S/B
point on List 3 is irregular both with respect
to the two adjacent datum points and with
respect to the corresponding data point on List
4. The discrepancy between and within lists at
various signal-to-babble ratios, especially for the
listeners with hearing loss, was an unexpected
finding. Earlier equivalency data reported on
the QuickSIN lists reported in the QuickSIN
manual (2001) were completed using low-pass
filtering of the QuickSIN lists on young
listeners with normal hearing to simulate
different degrees of high-frequency hearing
loss. The use of masking can control the
audibility factor of hearing loss and simulate
hearing loss (Plomp, 1978; Souza et al, 2003),
but masking may not mimic the distortion
factor of hearing loss (Dreschler and Plomp,
1985; Thibodeau, 1991).
The QuickSIN has gained some
popularity with clinicians because it is quick
and easy to administer; however, empirical
data have yet to establish the recognition
performance differences between the
QuickSIN and other measures of speech
recognition in noise that use stimuli with less
syntactic and semantic context, such as
monosyllables, to establish various levels of
recognition performance. In the current study,
not only was recognition performance for each
stimulus type examined, but performance
across the psychometric functions revealed
two interesting findings in terms of the type
of stimulus material. First, the psychometric
functions for the digit stimuli, which are a
special case of monosyllablic words, were
morphologically similar to the functions for the
word stimuli. The only difference was a “DC
shift” to the more favorable signal-to-babble
ratios by the function for the words. This
16–17 dB difference between performances on
words and digits has been described and
attributed to set-size differences (i.e., open- vs.
closed-set paradigms) and calibration
differences between digit and word materials
(Miller et al, 1951; Wilson, Burks, et al,
forthcoming). Although presented as triplets,
the digits provide a relatively closed set since
there are only nine possible items in each of
the three positions. Recognition performance
on a closed-set task, such as the one used in
this experiment with the digits, is expected to
be lower in terms of signal-to-babble ratio
than performance on an open-set task since
the number of items in the competition stage
of lexical access for a closed set is smaller,
thereby facilitating better recognition
performance at all signal-to-babble ratios
(Wilson and Antablin, 1982).
Second, Table 2 shows for both listener
groups the slopes for the psychometric
functions generated with the three stimulus
materials. The slopes at the 50% point for
words and digits are basically the same for
both groups of listeners (6 to 7%/dB). For the
listeners with normal hearing, the slopes of
the QuickSIN functions (14.0 to 15.8%/dB) are
about two times as steep as the slopes for the
digits (6.5%/dB) and words (5.8 to 6.7%/dB).
Additionally, the slopes of the QuickSIN
functions for the listeners with normal hearing
are about the same for both lists of materials.
The steeper functions for the QuickSIN
materials indicate that the performance on
sentences by the listeners with normal hearing
was more homogenous than the performances
on the word and digit materials. Perhaps the
syntactic and semantic context of the sentence
structure provided cues to the listeners with
normal hearing that were not completely
available to the listeners with hearing loss.
For the listeners with hearing loss, the
slopes of the functions for the two QuickSIN
lists are different, with the slope of the
function for List 4 the same as the slopes of
the functions for the digits and words, and the
slope of the function for List 3 more gradual.
Again, the data for List 3 of the QuickSIN are
reflecting the irregularities observed with
the data. Also, the slopes of the QuickSIN
functions for the listeners with hearing loss
are substantially more gradual than the slopes
of the functions for the listeners with normal
hearing. Since it is known that older listeners
are more vulnerable to the effects of noise
(Dubno et al, 1984) and show increased
difficulty processing information while
resisting the interference of noise (Willott,
1991), it is possible that the listeners with
hearing loss were unable to compensate with
top-down processing (syntactic and semantic
737
Journal of the American Academy of Audiology/Volume 16, Number 9, 2005
context), as were the listeners with normal
hearing.
In summary, as audiology moves toward
evidence-based practice guidelines, routine
clinical testing methods such as single-level,
monosyllabic word recognition in quiet need
to be reevaluated. Although it is important to
know how well (or poorly) patients understand
speech in quiet, it is equally important to
know how well patients understand speech in
noise, especially for rehabilitative purposes.
The digit, word, and sentence materials
presented in multitalker babble each provided
bimodal distributions of recognition
performances by listeners with normal
hearing and listeners with hearing loss. In the
current study the differences between groups
was ~8 dB in terms of signal-to-babble ratio.
Test-retest data for the digits in babble were
1.6 dB (normal hearing) and 1.2 dB (hearing
loss) and <1 dB for the QuickSIN and words
in babble. The two lists of the QuickSIN
materials produced psychometric functions
that morphologically were different. The data
for List 4 of the QuickSIN were systematic,
whereas the data for List 3 were irregular. An
examination of the homogeneity of the 18
QuickSIN lists on listeners with hearing loss
is currently underway in our laboratories.
The important findings in this study were
(1) that words and sentences presented in
background multitalker babble produce
recognition performances by listeners with
hearing loss that were equivalent (~12 dB
S/B), and (2) that digits, words, and sentences
provide the same ~8 dB differentiation between
performances by listeners with normal hearing
and performances by listeners with hearing
loss. This differentiation for the most part has
not been provided by performance on wordrecognition tasks in quiet.
NOTE
1. The 50% point reported by Walden and Walden
(2004) was 6.3 dB since Killion et al (2004) suggest
reporting QuickSIN scores in terms of SNR loss,
which entails subtracting 2 dB (average recognition
performance for normal-hearing listener) from the 50%
point obtained using the Spearman-Kärber equation.
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