PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Assessing the interaction between different auditory profiles andbenefit from six hearing aid processing strategies: Insights fromthe BEAR project
Mengfan WU1; Raul SANCHEZ-LOPEZ2; Mouhamad EL-HAJ-ALI1; Silje Grini NIELSEN2; Michal
FERECZKOWSKI2; Torsten DAU2; Sébastien SANTURETTE2,3 & Tobias NEHER1
1

Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
2

Hearing Systems, Dept. of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
3

Oticon A/S, Smørum, Denmark

ABSTRACTThe current study forms part of the Better hEAring Rehabilitation (BEAR) project, which aims to develop
and evaluate new clinical tools for individual hearing loss characterization and hearing aid (HA) benefit
assessment. The purpose of the current study was to assess the interaction between four different auditory
profiles and two measures of hearing aid outcome obtained for six selected hearing aid processing strategies.
Sixty older habitual hearing-aid users, who had previously been classified in four auditory profiles by a datadriven approach, participated in the study. All stimuli were generated with the help of a hearing aid simulator
and presented via headphones. Speech recognition in noise was assessed at fixed signal-to-noise ratios basedon individual 50%-correct speech reception thresholds measured in a realistic noise environment. Subjective
ratings of overall quality and noise annoyance were measured using a multiple stimulus comparison paradigm.
The four auditory profiles differed significantly in terms of the aided speech reception threshold (SRT) andinteracted significantly with the HA processing strategies for speech recognition when target speech waspresented at 90-degrees. Moreover, the correlations between the sentence recognition scores and subjective
ratings differed among the auditory profiles.
Keywords: hearing-aid processing strategy, auditory profiles, hearing-aid evaluation

1. INTRODUCTION
Although modern hearing aids typically come with various advanced signal processing schemes,
the treatment efficacy of these schemes in terms of hearing-aid (HA) fitting is still a matter of debate(1-4). A key issue is typically how much a given HA fitting can improve speech perception underadverse listening conditions (5). Even though the mechanisms governing speech understanding underadverse conditions are still not fully understood, it is important to investigate how benefit andpreference from different hearing aid processing schemes varies from person to person.
HA fittings are typically evaluated using both objective and subjective outcome measures.
Regarding objective measures, speech recognition in noise is the “gold standard”(5). A large numberof studies have investigated how specific HA fitting parameters affect speech recognition performance,
including directionality (e.g. (6, 7)), noise reduction (e.g. (8, 9)) and dynamic range compression (e.g.
(10)). Subjective hearing aid outcome has also received much attention in the research literature,mostly using questionnaires (11, 12). Despite them enjoy a number of advantages such as reflecting
real-life experience and having a well-established application in the clinic, there are disadvantages ofusing functional auditory assessments in evaluating HA outcomes. Especially for evaluating HAprocessing schemes, it is hard to verify and analyse how the processing of the acoustic input can affectthe perception of the participants. For those reasons, other forms of subjective measures appear to be
more suitable for HA evaluation. For instance, a multi-stimulus comparison test such as the wellknown MUSHRA paradigm (MUlti Stimulus test with Hidden Reference and Anchor, (13)) is a time1

awu@health.sdu.dk

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efficient subjective measure applicable for evaluating hearing devices, which is also suitable forlisteners with no technical experience (14-16).
It is well established that HA benefit can vary substantially across individuals. To achieve moreindividualized HA fittings, the BEAR project was recently initiated with the overall aim of developing
new clinical tools for individual hearing loss characterization and hearing aid benefit assessment.
Inspired by literature findings on supra-threshold hearing deficits, or “distortions”; e.g. (17-19),
Sanchez-Lopez et al. (20-22) developed a test battery and a data-driven approach for classifying
hearing-impaired listeners into four distinct auditory profiles. Under the assumption that these profiles
benefit from different HA treatments, the current study hypothesized that the four auditory profiles
would diverge in terms of their outcomes from different hearing aid processing strate gies. To test thishypothesis, six HAs were evaluated using different objective technical outcome measures to represent
different aspects of possible distortions in signal processing (23). In the present study, the selected
HA settings were experimentally evaluated by a group of listeners previously divided into four groups
based on their performance in an auditory test battery (22).
To achieve validity, hearing-aid outcome measures resulting from a perceptual evaluation shouldreflect real-life challenging noisy situations as much as possible (5). Older people with hearing loss
particularly struggle with communication in noisy environments, for example a dinner table scenario
with competing talkers and other distracting sounds (24, 25). Older listeners also find it difficult when
the target speech is not coming from the front (26).
Our experimental design included listening scenarios in two spatial conditions with target speechpresented from the front (0-degree) or from the side (90-degree) of the listener. Additionally, a speechlike distractor and realistic noise were included in stimuli. The hearing -aid outcomes were tested
through a sentence recognition task and subjective ratings. In order to make hearing -aid outcomescomparable among participants, individual speech reception thresholds ( ܴܵܶହ଴ ) were set as thebaseline performance. We hypothesized that listeners from different auditory profiles would responddifferently to 6 HA processing strategies in terms of sentence recognition scores and subjective ratings.
Besides, it is expected that listeners from the four auditory profiles would also be likely to respond to
the two spatial conditions differently for the same HA processing strategy.

2. METHODS
2.1 Participants
Sixty older participants (male = 30, female = 30) participated in the study. Their ages ranged from60 to 80 yr (mean = 70.8 yr). Twenty-nine of them were recruited and tested at Odense University
Hospital, Odense, while the other 31 were recruited and tested at Bispebjerg Hospital, Copenhagen.
Four participants quitted the study after the first visit. Prior to this study, all participants completed acomprehensive auditory test battery (21). Based on these measurements, they were classified into four
auditory profiles (22) : A (N = 14), B (N = 13), C (N = 20) and D (N = 8). The remaining five
participants could not be clearly allocated to any of these profiles and were therefore excluded fromall further analyses.
All participants had bilateral symmetrical sensorineural hearing loss and were habitual hearing-aid
users (> 9 months of experience). The range of hearing loss configurations was chosen to lie in between the N1 and N4 standard audiograms of Bisgaard et al. (27). The air-bone gap and interauralasymmetry in audiometric thresholds from 0.5-4 kHz were required to be maximally 15 dB. None ofthe participants had a history of any neurological or language disorders. All of them had self -reported
normal or corrected-to-normal vision.
2.2 Test setup
The measurements were performed in either an anechoic chamber (Odense Universit y Hospital,
Odense) or a soundproof booth (Bispebjerg Hospital, Copenhagen). Audio playback was via an RME
Fireface UC soundcard, an SPL Phonitor Mini amplifier and a pair of Sennheiser HDA200 headphones.
All stimuli were generated with the help of a hearing-aid simulator (HASIM) implemented in Matlab(see Sect.2.5). A touch screen was used by the participants for the subjective ratings.
2.3 Test procedure
Each participant was asked to come for two visits and each visit took approximately two hours. At
the beginning, ܴܵܶହ଴ was measured to control the baseline performance level between participants.

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For the ܴܵܶହ଴ and sentence recognition measurements, the participants were asked to repeat thesentences that they heard. The responses were scored by an experimenter. Prior to the ܴܵܶହ଴
measurements, there were two training trials. The thresholds were measured using a 1 -down 1-up
procedure with a step size of 4 dB for the first five presentations and 2 dB for the rest. The starting
signal-to-noise ratio (SNR) was 6 dB. The SRT was the average of the presentations from the fifth
and last sentence of the list. For the sentence recognition scores, there were 12 different measurements
in total (6 HA x 2 spatial conditions), and the same measurement s were repeated in the second visit.
Subjective ratings of overall quality and noise annoyance were collected separately between firstand second visits. A multi-stimuli comparison method with hidden anchor (MUSHA) as implemented
in the SenseLabOnline software from FORCE Technology was used. On each trial, participants were
presented with a graphical user interface containing seven playback buttons and sliders (6 HA settings+ 1 anchor stimulus) that allowed them to listen to the seven stimuli in any order and to rate the m ona scale from 0-100, where 100 being the best. Each stimulus was rated four times per spatial condition.
2.4 Stimuli
The target speech stimuli were DANTALE-II sentences spoken by a female native Danish speaker(28). There were 16 lists, with ten sentences in each list. All sentences were grammatically correctand had the same syntactical structure. The target speech was presented from either at 0º or 90º fromthe front towards the ‘better’ ear. The better ear was defined as the one with the best unaided speechscore as measured using the Danish Hearing In Noise Test (HINT) (29). The target was only played
from 0º for the ܴܵܶହ଴ measurement, while it was played from both 0º and 90º for the othermeasurements.
Two noise signals were used. Firstly, the International Speech Test Signal (ISTS; (30)) was used
as a directional distractor from either 90º (when target speech in front) or 0º (when target s peech fromthe side). To mimic a real-life situation, the second noise signal was a spatially diffuse cafeteria noise,
which was recorded in a university canteen with a pair of hearing-aid satellites as previously done in(22). The distractor was 2 dB SNR respect to the diffuse cafeteria noise. The tested signal-to-noise
ratio (‘test SNR’) for sentence recognition based on individual ܴܵܶହ଴ , so that the performance level
was relatively equal between participants. Due to technical limitations the ܴܵܶହ଴ was rounded and thetest SNR was fixed for the speech recognition conditions from a limited range of values. Therefore,
the test SNR ranged from -6 to 12 dB SNR in steps of 2 dB. The SNR applied for the subjective ratings
was test SNR + 4 dB for all six HA processing strategies and test SNR - 6 dB for the anchor. The
anchor for the subjective rating was distorted by random binary masker to simulate the spectral
distortion of the noise reduction scheme.
2.5 HASIM processing
The signal recorded from the frontal and rear microphones of a hearing aid was processed by abeamformer, a noise reduction algorithm and a wide-dynamic range compressor. The stimuli werelinearly amplified according to the NAL fitting rule for the ܴܵܶହ଴ measurements and the NAL-NL2
fitting rule for the remaining measures (31). Six HA processing strategies (see Table 1) were tested.
Apart from HA6, which resembles conventional HA settings, and HA1, which stands for very basic
processing, the other four HA contained aggressive processing features for different parameters tomaximize their potential differences. The strong noise reduction refers to an attenuation of 15 dB
while mild noise reduction applies 5 dB attenuation. HA6 corresponds to a hearing aid with similarparameters as a commercial HA. For further details of HASIM processing, see Sanchez-Lopez et al.
(23).

HA1
HA2
HA3
HA4
HA5
HA6

Table 1 – Description of the six tested hearing aid processing strategies
Directional processing
Noise reduction
Amplitude compression
Omnidirectional
Off
Slow
Omnidirectional
StrongFast
Binaural beamformer
Off
Slow
Binaural beamformer
StrongSlow
Binaural beamformer
StrongFast
Cardioid
Mild
Slow

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3. RESULTS
3.1 Data analysis
The median absolute deviation (MAD) method was used for identifying unreliable performancesin sentence recognition. The test-retest differences in the scores were examined for this purpose.
Difference scores exceeding r2 MAD were considered unreliable (32). In these cases, the lower scoreof the two test-retest measurements was excluded. Otherwise, the average scores of the test -retest
measurements were used and calculated into individual standardized scores. For the subjective ratings,
we used the eGauge method (33) to exclude participants who were unreliable raters. This left us with
51 participants (A = 13, B = 11, C = 19, D = 8) for overall quality and 48 participants (A = 13, B =12, C = 16, D = 7) for noise annoyance. Since each HA processing strategy was rated four times ineach spatial condition, the median of the four ratings was used to calculate individual standardized
scores.
For ANOVA and correlation analysis, the data was split based on the spatial condition for sentence
recognition data. In the 90-degree condition, the measurements from HA4 and HA5 were excluded
from the analyses because of a strong flooring effect (37.2% of the measures equal to 0). Statisticalanalyses for ANOVA were carried out using linear mixed effects (LME) models, implemented in R
using the lmer function from the lme4 package (34, 35). The dependent variable was the individual
standardized score. HA1 was set as the reference HA. The model also included the interaction between
HA and test SNR as well as the interaction between spatial condition and test SNR to account for the
influence from individual differences of SNR for the same HA. The random effect was the individualintercept. For subjective ratings, the dependent variable was also individual standardized score. The
model was similar to the one in sentence recognition except there were more interaction (see Table 3).
Correlation was tested through the Spearman method in R.
3.2 Influence of auditory profiles on test SNR
Figure 1 shows that the means of the test SNRs differed significantly between the four auditory
profiles. The test SNR of profile C was significantly different from all other profile s and profile A had
the lowest mean test SNR.

Figure 1: Means and 95% confidence intervals for the test SNRs of the four auditory profiles .
Comparisons were run by t-tests. The bars above represent the pairs that had significant differencein terms of means. Notes for significant level: *: p <= 0.05; ***: p <= 0.001; ****: p <= 0.0001.
3.3 Influence of auditory profiles on HA outcomes
Table 2 shows the results from the ANOVA performed on the speech scores for each spatialcondition. To simplify the analysis, the collected noise annoyance data were transformed so that betterscores corresponded to lower noise annoyance. Significant effects of HA processing strategies andinteractions with test SNR were shown in both outcomes. There was no significant effect of profile in3852

either spatial condition for all three measures. The significant effect of the interaction between HAprocessing strategies and Profile on sentence recognition performance appeared in the 90-degree but
not in the 0-degree condition (for details, see Table 2). There was no significant interaction between
HA processing strategies and Profile on subjective ratings.
Table 2: Results from two ANOVAs performed on the sentence recognition scores.
0 degree90 degree
HA
df
5
3
F
36.7
102.3
p
<0.001< 0.001
Auditory
df
3
3
F
<0.1
0.4
profile
p
0.99
0.71
df
15
9
HA u
F
0.9
4.3
auditoryp
0.55<0.001
profiledf
6
4
HA u test
F
0.8
6.4
SNR
p
0.56
<0.001
Table 3: Results from the ANOVAs performed on the subjective ratings.
Overall Quality
Noise Annoyance
HA
df
5
5
F
41.8
15.6
p
< 0.001<0.001
η2
Spatial condition
df
1
1
F
176.4
81.3
p
< 0.001< 0.001
Auditory profile
df
3
3
F
0.4
0.3
p
0.75
0.83
df
5
5
HA u spatial condition
F
61.9
28.2
p
< 0.001< 0.001
df
15
15
Auditory profile u HA
F
1.6
1.1
p
0.08
0.30
df
3
3
Auditory profile u
F
2.9
1.3
spatial condition
p
0.03
0.26
df
6
6
HA u test SNR
F
8.5
3.9
p
<0.001<0.001df
1
1
Spatial condition u test
F
3.5
4.2
SNR
p
0.03
0.04
df
15
15
Auditory profile u HA
F
0.9
1.4
u spatial condition
p
0.47
0.13
3.4 Correlations between speech scores and subjective ratings for each profile
In general, there were more significant (positive) correlations between the subjective ratings andspeech scores in the 90-degree condition than in the 0-degree condition. Especially between the
response of overall quality and sentence recognition scores, all profiles showed relatively large

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positive correlations (all ‫ ݎ‬൒ 0.4, all ‫ ݌‬൏ ͲǤͲͳሻ in the 90-degree condition while only one profile
showed significant correlation in 0 degree (36). Profile B consistently showed typical or relatively
large positive correlations between sentence recognition and the subjective ratings (all ‫ ݎ‬൒0.29, all‫ ݌‬൑ ͲǤͲͶ). Profile C showed significant correlations for most of the conditions while profile D only
had significant correlations in the 90-degree condition. For profile A, this was generally not the case.

4. DISCUSSION
The results of our analyses of the interaction between HA processing strategies and auditory
profiles were not as expected. Overall, there were no evident links between the four auditory profiles
and outcomes from the six tested HA processing strategies. One possible reason is that we tested ourparticipants based on individual ܴܵܶହ଴ measurements. This could have ‘evened out’ the differences
between the four profiles. As shown in Figure 1, the test SNRs differed significantly between the
profiles. Setting ܴܵܶହ଴ to begin with was mainly because we wanted all participants to have arelatively similar starting point in performance level. However, the individualized SNR of the stimuli,
referred to test SNR in the previous section, can result in differences in terms of processing of the
input signal within the same HA algorithm. For example, compression acts differently on different
ranges of input level. Given that the mean test SNR of profile A was around 5 dB lower than profile
C, the algorithms of the six HA probably worked differently between these two groups of participants.
On the other hand, hearing aid users often encounter all sorts of SNR situations in their daily life, and
it is not clear which of these affect a user’s satisfaction the most. Another explanation could be thatsome of the tested HA were simply too ‘aggressive’ for all profiles, as indicated by the clear flooring
effects in the 90-degree condition for sentence recognition in noise. Finally, despite using the same
fitting formula for gain prescription, it could also be that people from different auditory profiles havegreater benefits from different kinds of amplification (37).
The correlation analyses that we performed produced some interesting findings. Some profiles
showed more positive correlations between sentence recognition scores and subjective ratings thanothers. The consistent positive correlations observable for profile B probably indicate that these users
will be satisfied with their hearing-aid processing if speech clarity is guaranteed. For Profile A users,
on the other hand, much less correlation between objective and subjective outcomes might imply thatthey are more demanding in terms of sound naturalness. As for profile C, it seems that noise annoyanceis a more informative measure to predict their preference.
Moreover, the consistent correlations between overall quality and sentence recognition in the 90 degree condition also suggest that some relatively simple and quick subjective measures could be very
informative. Clinical measures used during hearing aid fitting are mostly about verification (e.g. realear measures); little, if any, information is regularly collected about subjective preference for different
processing schemes (38). For audiologists and hearing-aid dispensers, the choice of processing
scheme is mostly based on personal experience or manufacturer’s guideline (39). Given various
individual differences for hearing-aid preference, a short and informative measurement for validation
at the beginning is possible to improve the efficiency of the hearing aid fitting procedure.

5. CONCLUSIONSThe current study used two types of outcome measures for evaluating the interaction between four
auditory profiles and six different hearing-aid processing schemes. The results indicated that theauditory profiles influenced HA outcome only in the 90-degree condition. This could have been aconsequence of the experimental design that was used. Especially, individualized SNR of the stimuli
according to their ܴܵܶହ଴ might decrease the differences between auditory profiles to some extend .
Since testing each listener at their ܴܵܶହ଴ is not realistic, efforts should be made to provide an
additional SNR benefit, by signal processing, to the profiles which showed elevated SRTs. The
auditory profiles differed somewhat in terms of their correlations between the spe ech scores andsubjective ratings, which may suggest that the speech understanding can drive their subjective
perception in one group while others might be influenced by the comfort, listening effort or otheraspects related to their hearing experience

ACKNOWLEDGEMENTS
This work was supported by Innovation Fund Denmark Grand Solutions 5164 -00011B (Better
hEAring Rehabilitation project), Oticon, GN Resound, Widex and other partners (Aalborg University,3854

University of Southern Denmark, the Technical University of Denmark, Force, Aalborg, Odense and
Copenhagen University Hospitals). The funding and collaboration of all partners is sincerely
acknowledged. The authors sincerely thank Rikke Skovhøj Sørensen (Technical University ofDenmark) and Christer P. Volk (Force Technology) for their support. We also want to thank theparticipants and student helpers that took part in the study.

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