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RTI!R0I-JS[N2_Ǟ]vn2R\};i߈'lڶqnO8jI@`"*覫'K/R &0<ި4° T:R[ !xc559XhMFrJ#ɡp^0KK!ZqLF6*fxrg.0K:TWps*̷ږ+'F!ÍJZɥFA(uE= H/ S]UQ!,#/_wB`D"ᢤ+  G`M &@2 㘒E5& )w{i`rd^AL\ֺ $t,M%{D|mZL n\]ٌ&RIYxeaL O! %`4I=X j[4Sm 㿥_ EI| %PXo-`#*Hy/舨 Q@O0@\ H080F4?wd-Jf+MJ #-Qϴ"d/:F@F_ %I\I AEېˆ!t)!I2 @\ɪˁU@քvL|]@%6V\ʼDh verification process using this subset of sentences was then derived. Thus, each
run of the sentence verification test yielded an identification score out of 200
(here expressed as percent correct) and a response time(verification time) for
the decision regarding the sense/nonsense of the sentence.


Subjects


The five subjects were all established users (at least 12 months) of a single
post-aural BE10 series National Health Service hearing aid. The characteristics
of the subjects are shown in Table 1 (subjects 14-18). They all had broadly
symmetric bilateral sensorineural losses of moderate degree, with greater losses
at high frequencies than at low. All subjects had taken part in earlier
experiments using the sentence verification test and were familiar with its form
and configuration.


Experimental Design


The experiment consisted of five sessions for each subject, usually conducted at
weekly intervals. Each session used seven complete runs of the sentence
verification test as configured above. During each session, data were gathered
for a pair of signal-to-noise ratios for each of the three processing conditions
(E0, ENH, and ENHC10/2). An initial complete run for one of the signal-to-noise
ratio/processing conditions was employed as practice, as previous experience with
the sentence verification test suggested that optimal stability is achieved if
this is done. The signal-to- noise ratios for each session were selected from a
blocked design across subjects. Within each signal-to-noise ratio, the order of
the three conditions was selected randomly. During the course of the five
sessions, each subject was tested twice for each of the signal-to-noise ratios
and each of the processing conditions.


Results


To show the overall form of the results, the mean of the two repetitions for each
subject/signal-to-noise ratio/condition combination was taken and then the scores
for the five subjects were averaged. The results are summarized in Figure 5.
Error bars show 95 percent confidence limits. The figure shows the expected trend
of increasing intelligibility and decreasing response times as the
signal-to-noise ratio increases. For the identification component, there appear
to be modest but consistent advantages at most signal-to-noise ratios for both of
the processed conditions over the control condition (E0). For the response time
component, the advantages of the processing conditions are larger, relative to
the confidence limits, and there is a clear tendency for the processing condition
involving both enhancement and compression (ENHC10/2) to give shorter response
times than the condition involving enhancement alone (ENH).


The results of the five subjects were subjected to a repeated-measures ANOVA,
using the GENSTAT package, with the following dependent variables: (i) percent
correct score; (ii) arcsine(square root of-proportion correct)--this measure
makes the scores follow a normal distribution more closely; (iii) response time;
and, (iv) square root of response time--again, this measure makes the scores
follow a normal distribution more closely.


The results for the transformed variables (ii) and (iv) were similar to those for
the untransformed variables (i) and (iii), so the latter will be presented to
facilitate interpretation. In the ANOVA, there were three within-subject factors.
The independent variables were: (i) the signal-to-noise ratio (0, 3, 6, 9, and 12
dB); (ii) the condition (linear, enhanced, enhanced and compressed); and, (iii)
replication (first and second replicate).


For the percent correct scores, there was a highly significant effect of
signal-to-noise ratio [F(4,16) = 97.1, p < 0.001], as expected from Figure 5, and
a significant effect of condition [F(2,8) = 6.65, p < 0.021. The main effect of
replicate was not significant, and none of the interactions was significant. The
mean score for condition ENH was 1.76 percent greater than that for condition E0
(standard error = 0.54), and this difference was statistically significant (p <
0.02). The mean score for condition ENHC10/2 was 2.73 percent greater than that
for condition E0, and again this difference was significant (p < 0.001). The mean
difference between conditions ENH and ENHC10/2, 0.97 percent, was not
significant.


The ANOVA for the response time component of the sentence verification test
showed highly significant effects of signal-to-noise ratio [F(4,16) = 333.6, p <
0.001] and of condition [F(2,8) = 31.4, p < 0.001]. The main effect of replicate
was not significant, but there was a significant interaction between
signal-to-noise ratio and processing condition [F(8,32) = 4.07, p < 0.002],
consistent with the greater effect of condition at low signal-to-noise ratios
apparent in Figure 5. The mean response time for condition ENH was 62.8 ms less
than that for condition E0 (standard error = 10.1 ms), and this difference was
statistically significant (p < 0.001). The mean response time for condition
ENHC10/2 was 113 ms less than that for condition E0, and again this difference
was significant (p < 0.001). The mean difference between conditions ENH and
ENHC10/2, 52.8 ms, was also significant (p < 0.001). Thus, the results show that
there are significant advantages for the processed conditions compared with the
control condition, with combined enhancement and compression giving bigger
advantages than enhancement alone. The advantages are statistically more robust
for the response-time component of the test than for the identification
component.


The magnitudes of the effects described above, especially the response times, are
difficult to interpret because of the somewhat complex nature of the sentence
verification test. One way of relating the effects to other, more familiar
measures, is to convert the differences in percent correct scores or response
times to equivalent changes in signal-to-masker ratio. The data in Figure 5
indicate that both the percent correct scores and the response times are
approximately linearly related to the signal-to-noise ratio, for signal-to-noise
ratios between 0 and +6 dB. For the control condition, each 1-dB increment in
signal-to-noise ratio produces a 2.3 percent change in the percent correct score
and a 38.3-ms change in the response time. These relationships were used to
transform the magnitudes of the differences between conditions into equivalent
changes in signal-to-noise ratio in dB.


For the percent correct scores, the difference between conditions E0 and ENH was
equivalent to a 0.8-dB change in signal-to-noise ratio, while the difference
between conditions E0 and ENHC10/2 was equivalent to 1.2 dB. For the response
times, the difference between conditions E0 and ENH was equivalent to a 1.6-dB
change in signal-to-noise ratio, while the difference between conditions E0 and
ENHC10/2 was equivalent to 3.0 dB. Thus, the benefits of processing are
approximately twice as large for the response-time component as for the
identification component. If percent correct and response time can be regarded as
subcomponents of an overall benefit from processing, then condition ENH gave an
overall benefit of 2.4 dB compared with the control condition, and condition
ENHC10/2 gave an overall advantage of 4.2 dB compared with the control condition.


The fully factorial, repeated-measures nature of the experimental design enabled
individual differences to be investigated. For the identification scores, a
general linear model (GLIM) analysis was conducted based on a logistic model
assuming that errors were distributed according to a binomial distribution. Here
the proportion correct (PrC) is the dependent variable in an equation of the
form:


PrC = 1/(1 + exp(- (B0 + B1*X1 + B2*X2 +. . .))) [6]


where X1, X2, etc. are indices for specific values of the independent variables
(signal-to-noise ratios, and processing conditions) and their interactions. The
procedure produced estimates of the values of the parameters, B0, B1, B2, etc.,
referenced to a specific baseline, namely the mean for the control condition at 0
dB signal-to-noise ratio. B0 is the parameter estimate for the baseline itself.
The effect of replicate was not significant and is not included in the analysis.
The results are summarized in Table 6.


To return to a percent correct score from a parameter estimate, the equation


Percent correct = 100/(1 + e- (sum of estimates)) [7]


is used. Thus, for subject 14 the figure of 0.315 for the baseline (control
condition at 0 dB signal-to-noise) is equivalent to a score of 57.8 percent.
Using the properties of the logistic regression, the effect of a combination of
factors may be assessed by simple addition of the parameter estimates. Thus the
estimate associated with a signal-to-noise ratio of 3 dB in the control condi