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The following article appeared in
Kokabi, O., Brinkmann, F., & Weinzierl, S. (2018). Segmentation of binaural room impulse responses for
speech intelligibility prediction. The Journal of the Acoustical Society of America, 144(5), 2793–2800.
and may be found at
https://doi.org/10.1121/1.5078598
Omid Kokabi, Fabian Brinkmann, and Stefan Weinzierl
Segmentation of binaural room impulse
responses for speech intelligibility
prediction
Accepted manuscript (Postprint) Journal article |

Segmentation of bin aural room impulse res ponses for speech
intelligibility predict ion

Omid  K okabi,
a) 
F abian  Brinkmann,  and  Stefan  W einzierl
TU  Berlin,  Audio  Communication  Group,  Einsteinufer  17c,  10587  Berlin,  Germany
The  two  most  important  aspects  in  binaural  speech  perception—better-ear-listening  and  spatial-
release-from-masking—can  be  predicted  well  with  current  binaural  modeling  frameworks  operating 
on  head-related  impulse  responses,  i.e.,  anechoic  binaural  signals.  To  incorporate  effects  of  rever-
beration,  a  model  extension  was  proposed,  splitting  binaural  room  impulse  responses  into  an  early, 
useful,  and  late,  detrimental  part,  before  being  fed  into  the  modeling  framework.  More  recently,  an 
interaction  between  the  applied  splitting  time,  room  properties,  and  the  resulting  prediction 
accuracy  was  observed.  This  interaction  was  investigated  here  by  measuring  speech  reception 
thresholds  (SRTs)  in  quiet  with  18  normal-hearing  subjects  for  four  simulated  rooms  with  different 
reverberation  times  and  a  constant  room  geometry.  The  mean  error  with  one  of  the  most  promising 
binaural  prediction  models  could  be  reduced  by  about  1  dB  by  adapting  the  applied  splitting  time  to 
room  acoustic  parameters.  This  improvement  in  prediction  accuracy  can  make  up  a  difference 
of  17%  in  absolute  intelligibility  within  the  applied  SRT  measurement  paradigm.
I. INTRODUCTION
The most important binaur al mechanisms for the per-
ception of speech in acoustic environments with competing
noise sources are better-ear listening and binaural unmasking
of spatially separated sources ( Middlebro oks et al. , 2017 ).
Head shadowing and the ears’ spatial sensitivity cause dif-
ferent signal-to- noise ratios (SNRs) at the listeners’ left and
right ear. Better-ear listening refers to the fact that the audi-
tory system primarily extracts information from the ear sig-
nal with the more favorable signal-to-noise ratio ( Edmonds
and Culling, 2006 ). Binaural unmasking refers to reducing
the strength of a masking sound source on a speech target
when the two are spatially separated ( Kock, 1950 ). Although
there is no clear interpretation of how both mechanis ms are
exactly combined in the auditory system, additivity proved
to be a successful candidate ( Jelfs et al. , 2011 ).
Differ ent audito ry models have been develo ped to repre-
sent the tw o mechan isms. Amo ng these, the Oldenb urg
model ( Beu telman n and Brand, 200 6 ; Beutelm ann et al. ,
2010 ) an d the Cardif f model ( Jelfs et al. , 2011 ; Lavandi er
and Culli ng, 2010 ) seem to be most pro mising ( Culli ng et al. ,
2013 ). Bot h models co mbine an SNR/ speech int elligibil ity
index (SII) ( ANSI S3.5, 1997 ) based better-e ar evalua tion
with a mod eling stage for bin aural unmask ing based on the
equali zation- cancella tion (EC) the ory ( Durlac h, 1963 ). The
model inp ut is either a binaura l stream of the speech an d
masker ea r signals, or a bin aural roo m impuls e respon se
(BRIR), des cribin g the tra nsfer path between the spe ech an d
masker sou rces and the human receive r.
In typical rooms, the speech signal is a combination of
the direct signal, a series of ear ly distinct room reflection s
and late diffuse reverberation. While distinct reflections
shortly following the direct sound are generally considered
to improve speech intelligibility ( Bradley et al. , 2003 ),
reverberation is known to have a detrimental effect by
increasing the temporal masking due to a reduced depth in
the temporal modul ation of running speech.
In both models mentioned above, however, the entire
speech signals is considered as useful, thus ignoring the det-
rimental effect of reverberation on speech reception. To
account for this, it was proposed to split the BRIR into an
early, useful and a late, detrimental part ( Rennies et al. ,
2011 ), referred to as the U/D-approach in the remainder of
this document. Both parts are fed separately into the model
and are considere d as the speech target and as an additional
masker. The U/D-concept can also be found in many room
acoustic parameters such as Clarity C
80
or Definition D
50
,
which are used to predict the transparency of speech and
music (ISO 3382-1, 2010). Howev er, different U/D-limits
ranging from 35 to 95 ms are applied ( Bradley, 1986 ;
Lochner and Burg er, 1964 ).
By extending the Oldenburg model with a U/D-
approach, the prediction accuracy could be improved both
for a simple case consisting of a direct signal and one lateral
or frontal reflection ( Rennies, 2014 ) as well as for a more
complex sound field with non-negligible levels of reverb era-
tion ( Rennies et al. , 2011 ). Improved performance was also
observed for the U/D- extended Cardiff model ( Lecle ` re et al. ,
2015 ). The optimal U/D-lim it was found to depend on the
properties of the room, which was considered as a general
downside of this approach. A link between the respective U/
D-limits and room acoustic pr operties, however, was not
investigated so far.
The present work tries to fill this gap by predicting opti-
mal U/D-limits for different room acoustical environments
a)
Electronic mail: [email protected]

and source-receiver configurations using ro om acoustic
parameters, thus increasing the precision and the generaliz-
ability of binaural models for speech perception. Therefore,
SRTs in quiet were measured for a virtual room with system-
atically varied acoustic properties.
II. METHOD
A. SRT measurements
1. Subjects
Eighteen native German speakers (13 male, 5 female;
average age 30.4) with normal hearing [ISO 8253-1 hearing
levels (HLs) between ÿ 10 and þ 20 dB HL] participated in
the tests on a voluntary basis. Except for two, all subjects
had experience with psychoacoustic listening tests.
2. Procedure
The Ol denb urg s entenc e tes t (OLSA ) ( K €
uhnel et al . ,
199 9 ; Wage ner et al . , 199 9a , b ) w as used to m easur e SRTs
in qu iet , i.e., w ith out ad ditio nal m aski ng no ise sou rce s, by
find ing t he s ound pressu re le vel that i s req uir ed for 50% co r-
rec tly un ders tood w ords . For t his pur pose, test sen ten ces
cons ist ing of fi ve wor ds at a n atural s peec h rat e with a fix ed
syn tax (na me– verb– num ber– adject ive– objec t) bu t unpr e-
dic table seman tics were presen ted t o the partic ipa nts. Th e
par tici pant s were as ked to rep eat th e tes t sente nce, af ter
whi ch the expe rimen ter adap tiv ely adju sted th e level of the
suc cess ive s entenc e ac cordi ng to t he num ber of cor rect ly
und ersto od wo rds i n step s from 6 1 t o 6 3 dB for senten ces
2–5 , and f rom 6 1 t o 6 2 dB fo r sen ten ces 6– 31 ( H €
orTech
gGm bH, 201 1 ). Th e test co nver ges at the S RT (50% co r-
rec tly under sto od word s) withi n a set of 30 tes t sentenc es
per co nd ition . The O LSA co rpu s is co mpri sed of 12 0 diff er-
ent s ent ence s, whi ch ar e com bine d int o 40 te st lis ts of 30
sen tence s per l ist.
Rennies et al. (2011) found a significant correlation
between pure tone thresholds and measured SRTs in quiet
even for liste ners with normal HLs < 20 dB HL, i.e., subjects
with lower overall hearing sensitivities tend to show higher
( ¼ worse) SRTs. As the current study focused on the effect
of reverberation on SRTs and not on the effect of hearing
sensitivity, it seemed desirable to compensate the measured
SRTs for the latter to achieve a clearer display of the experi-
mental data. To do so, HLs were measured for every subj ect
by means of individu al pure tone audiograms for both ears
and frequencies between 125 Hz and 8 kHz ( IEC 60645-1,
2017 ; ISO 8253-1, 2010 ). For each subj ect, the pure tone
average (PTA ¼ mean dB HL) at 0.5, 1, and 2 kHz was cal-
culated taking the ear data with the lower hearing level per
band (assuming better-ear listening in speech perception).
These better-ear PTAs ran ged between ÿ 6 dB HL (most se n-
sitive subject) and þ 8 dB HL (least sensitive subject). To
compensate the SRTs for these inter-in dividual differences,
the better-ear PTAs were subtracted from the measured
SRTs. A correlatio n analysis between each subjects better-
ear PTA and his/her mean SR T across condition s revealed a
high correlation ( r  0.71, p < 0.001), confirming the find-
ings by Rennies et al. (2011) .
Four test conditions with different acoust ic conditions
discussed below with 30 sentences per condit ion were pre-
pared for every participant. The participants were positioned
in a hemi-anechoic chamber at TU Berlin with the experi -
menter in the adjacent control room. The stationary nois e
level in the hemi-anech oic chamber was below 20 dB(A)
(logged during the entire session with an NTI XL2 sound
level meter, NTI MA220 Mic-preamp, and an NTI MA2230
microphone, calibrat ed via Larson Dav is CAL200 acoustic
calibrator). The stimuli were played back via a Focusrite
Scarlett 18i20 USB interface, and closed, circumaural
Beyerdynamic DT770 Pro headphones. The headphones
were calibrated to absolute s ound pressure levels via a B&K
Artificial Ear type 4152, a preamplifie r B&K type 2609, and
a B&K sound level calibrat or type 4230 . Audio playback
was controlled by a laptop ru nning MATLAB in the control
room. For the audiogram test, the participant directly
responded via a generated MATLAB user interface. For the
SRT measurement, the participant made a spoken response
via an Omnitronic GMTS100 intercom terminal with talk-
back microphone.
The test started with the pure tone audiogram, followed
by the SRT measu rements for the four test conditions in ran-
domized order. To familiarize the participants with the task
and the stimuli, training was performed pr ior to the actual
tests. The procedure with instructi on, training, and filling out
the questionnaire took about 70 min per participant.
The (re)-positioning of headph ones slightly changes the
frequency-dependent stimul us level at the listener’s ear
drum and causes an uncertainty in pure tone audiometry
( Paquier et al. , 2012 ) and an audible coloration of the stimu-
lus ( Paquier and Koehl, 2015 ). To reduce this source of
error, the participant s were instructed to not move or touch
the headphones during the entire test. This way, the mea-
sured hearing levels are sufficiently accurate with respect to
the presentation level of the OLSA sentences.
3. Stimuli
The physical response of a room is characterized by the
reflection pattern (temporal structure and amplit ude) arriving
at the listener’s ears. While the temporal structure is related
to the room geometry and the positions of source and
receiver, the amplitudes of the individual reflections are
mainly determined by the boundary condition s (absorption,
scattering) of the surfaces. To be able to independently vary
the room geometry and surface properties, all BRIRs were
simulated with the geometrical acoustics software RAVEN
( Schr €
oder and Vorl €
ander, 2011 ). The acoustic environment
for which BRIRs were generated was based on the geometry
of an existing, medium sized auditorium with shoebox
design featuring diffusing wall and ceiling elemen ts with an
elevated stage and an audience area (Fig. 1 ).
In a first step, BRIRs for seven different room config ura-
tions were simulate d by scaling the room size and absorption
coefficients (combinations of four volumes V ¼ {500, 1000,
2000, 4000} m
3
at a fixed reverberation time of T
20, m
¼ 1 s
and four reverberation times T
20, m
¼ {0.5, 1, 2, 4} s at a fixed
volume of V ¼ 1000 m
3
). An informal listening test showed

a stronger impact on speech intelligibi lity when scaling the
absorption coefficient s for a room of fixed size than vice
versa. As a consequen ce, the SRT me asurements were con-
ducted for four conditions with varying reverberation times
by scaling the surface absorption coefficients. Absorption
values maintained a typical behavior both in size and in fre-
quency dependence under all test conditions.
BRIRs were calculated for a source at the center of the
stage and a binaural receiver in the audience area at a dis-
tance of approximately 9 m corresponding to about three
times the critical distance at the lowest reverberation level.
For the source, the directivity of a male singer was applied
(average directivity index Q ¼ 1.5 for 500 Hz and 1 kHz
octaves). Measured head related transfer functions (HRTF s)
of the FAB IAN head-and-torso simulat or with a resolution
of 2  in azimuth and elevation were used as receive r directiv-
ity ( Brin kmann et al. , 201 7b ). Binaura l auralizat ions of the
OLSA sentenc e corpus were calcul ated via convol ution with
the genera ted BRIRs. To avoid colorat ion due to the fre-
quency resp onse of the headph ones, we used an inverse filter
of the Beyerdy namic DT770Pr o headphone s from the
FABIAN dat abase ( Br inkmann et al. , 201 7a ).
The applied absorption and scattering coefficients as
well as the resulting frequency dependen t reverberation sta-
tistics and the calculated BRIRs (headphone filter not
applied) are accessible in Kokabi et al. (2018) .
B. SRT prediction
1. Gene ral prediction procedure
windo ws: an early window consisted of a flat (weight ¼ 1)
part from the time of arrival up to the considere d U/D-limit,
and a linear fade-out with a length of 1 ms. A late window
starting with zeros up to the considered U/D- limit, followed
by a fade-in of length 1 ms, and continued with a flat part
(weight ¼ 1) until the end of the BRIR. The early (useful)
part was used to generat e the speech target and the late (det-
rimental) part was used to generat e the masker. Both were
separately fed into the model.
The model output is a SNR in dB pr edicting the benefit
of binaural listening over listening to an omnidirectional
receiver at the same position. As suggested by Jelfs et al.
(2011) , the predicted benefit was converted to an SRT by a
multiplication by ÿ 1, and by scaling every benefit by the
same factor until the average across all pre dictions matches
the average across all measured SR Ts. By doing so, the
model output can directly be compared to measured SRTs in
the respective condition. It is important to note that the
model is only able to predict relativ e SRT differences
between test condit ions due to the matching of the means of
measured and predicted data. Due to the fact that only rela-
tive differences between conditions can be pr edicted by the
model, the compensatio n applied to the measured SRTs
based on each subjects’ better-ear PTA (cf. Sec. II A 2 ) has
no effect on the prediction accuracy of the model. In addi-
tion, the prediction accuracy with fixed and room-d ependent
U/D-limits was also tested for an external datase t with SRTs
in quiet measu red for two conditions S0 (so urce in front of
the listener) and S90 (source to the right of the listener ) in a
virtual rectangular ro om (length: 10 m, width: 15 m, height:
3 m) wit h rev erberation times of about 2 s, simulated with
CATT-Acoustic v8. The rationale for incorporating this
additional dataset in the present evaluation was to fu rther
validate the derived pr ediction method on data wh ich were
not part of the derivation process. The two test conditions of
the external dataset each feature four source-receiver distan-
ces, ranging from d ¼ 0.5 m to d ¼ 13.0 m ( Rennies et al. ,
2011 ). This dataset is referred to as RS11 in the remainder of
this document.
2. Fitted U/D-l imits
U/D-limits fitted to the measured SRT values were
determined by calculating SRT predictions with the method
given above, whereb y for every condit ion (BRIR), 19 differ-
ent U/D-limits from 20 to 200 ms wit h 10 ms steps were
used, resulting in 19
4
predicted SRT sets for each participant
of the listening test. All U/D-limits leading to a mean abso-
lute error (MAE) between mea surement and prediction of
< 1 dB across all four condit ions were selected. From this
subset, the mean was calculated for each test condition, and
taken as the fitted U/D-limit. Since differences between
MAEs were qui te small, this method was regarded as more
robust than considering only the U/D-combin ation with the
smallest MAE.
3. Room acou stical prediction of U/D-limits
To predict U/D-limits from room acoust ic parameters, a
linear regression analysis was performed with the room
FIG. 1. Three-dimensional room model ( V ¼ 1000 m
3
) with dimensions and
source/receiver position. The gray shade denotes the surface type (seating
and residual).
The  generated  BRIRs  were  applied  to  the  Cardiff  binau-
ral  model  ( Jelfs  et  al. ,  2011 )  implemented  in  the  auditory 
modeling  toolbox  ( Søndergaard  and  Majdak,  2013 ).  The 
Cardiff  model  was  chosen  due  to  (a)  its  computational  effi-
ciency,  (b)  its  open  source  availabil ity,  and  (c)  the  fact  that 
no  parameter-fitting  is  involved  in  the  implemented  model-
ling  stages  for  better-ear  listening  and  binaural  unmasking—
apart  from  the  JND-jitter  implementation  introduced  in  the 
original  EC-model  ( Durlach,  1963 ).  The  model  was 
extended  by  a  temporal  U/D-classificatio n  as  suggested,  e.g., 
by  Rennies  et  al.  (2011) ,  implemented  by  the  authors.  For 
the  latter,  each  BRIR  was  multiplied  with  two  time

acoustic parameters as independent var iables, and the fitted
U/D-limits as dependent variable. Since binaur al de-
reverberation in speech perception was shown to be corre-
lated to monaural acoust ic parameters as well as binaural
parameters assessing the similarity between both ear signals
( Ellis et al. , 2015 ), three parameters were used as predictors
in the regression analys is: Clarity ( C 80
m
, ISO 3382-1, 2009 )
and the direct-to-reverbe rant energy ratio ( D / R ) as monaural
predictors, and IACC
m
as a binaural predictor, where m
denotes the average over the 500 Hz and 1 kHz octave
values. The room acoustic parameters D / R and C 80
m
were
calculated from ro om impulse response (RIRs) with omnidi-
rectional source and rece iver directivities at the same posi-
tions used for the BRIR calculation. In case of the data from
RS11, these parameters were calculated from the BRIRs
(mean across ears), as mo naural RIRs were not available.
The IACC was always calculated from the BRIRs. C 80
m
and
IACC
m
were calculated using the ITA-Toolbox ( Dietrich
et al. , 2010 ). D / R was calculated as the energy ratio of the
direct to reverberant part of the RIR with a time limit of
2.5 ms to separate the two parts ( Zahorik, 2002 ).
The results of the regression analysis were then used to
predict U/D-limits from the room acoustic parameters. These
predicted U/D-lim its were tested against two fixed U/D-lim-
its: 50 ms (reco mmendation in ISO 3382-1 for Clarity for
speech) and 100 ms (better prediction than wit h 50/ 80 ms in
Rennies et al. , 2011 ).
III. RESUL TS
Measured SRTs and predicted SRTs with predicted and
two fixed U/D-limits (50 ms and 100 ms) are shown in Fig. 2
for all four test conditions and averaged across participants.
The MAE a vera ged ac ross te st con diti ons is gi ven in Tabl e I .
To test for syste matic differences in measured SRT data
between condition s, a one-way repeated measures analysis
of variance (ANOV A) with a significance level of 0.05 and
Greenhouse-Geisser correction was applied. The results
reveal a signifi cant effect of the level of reverberation on
the measured SRTs for the four test conditions [ F (1.4,
24.4) ¼ 206.3, p < 0.001]. Post hoc tests using Bonferroni
correction revealed that the meas ured SRTs at all tested lev-
els of reverberation were significantly different from each
other ( p < 0.001). For completeness, the statistical analys is
was repeated without compensation of the SRT data where
the ANOVA also showed a significant effect of level of
reverberation on measu red SRTs [ F (1.4, 24.4) ¼ 206.3,
p < 0.001]. As in the case with SRT compensation, post hoc
tests using Bon ferroni correction revealed that measured
SRTs at all tested levels of reverberation were signific antly
different from each other ( p < 0.001).
A. Fixed U/D-limits
The data of the current stud y (Fig. 2 , left) show that
measured and pre dicted SRTs with fixed U/D-limits increase
with increasing level of reverberation. Compa ring the pre-
diction accuracy with fixed U/D-lim its, it can be seen that
the error for U/D ¼ 50 ms (MAE
mean
¼ 1.9 dB) is slightly
lower than with U/D ¼ 100 ms (MAE
mean
¼ 2.9 dB) fo r the
data from the current study. However, this trend is reversed
for the RS11 data (U/D ¼ 50 ms: MAE
mean
¼ 2.6 dB;
U/D ¼ 100 ms: MAE
mean
¼ 2.0 dB), cf. Table I .
Becaus e the ab solute level of the predict ed SRTs has to
be manuall y matche d to the mea sured SRTs, only SRT dif fer-
ences betwe en test condit ions can be pre dicted by the mod el.
They can be ded uced from the gra dient of the lines connec t-
ing any two test condit ions. The under-/ overes timation of
SRT increa se with both the data from the current study and
the RS1 1 data and the predict ion mod el with the fixed U/D -
limit s is depict ed in Fig. 3 . For the current study, the SRT-
increa se is overestim ated in the low and medium reverbe rant
condit ions (0.5  T
20, m
 2 s), but underesti mated between
the condit ions with T
20, m
¼ 2 s and T
20, m
¼ 4 s for both fixe d
U/D-li mits. A similar trend can be obs erved for the SRTs
measure d at different sou rce distanc es (RS11 data) where
larger ove restima tions can be ob served betw een condit ions
for dis tances below 3.5 m. For source distanc es between 3.5
and 13 m, qu ite accur ate predict ions ca n be observe d with
both fixed U/D-li mits (under-/ov eresti mation < 1 dB).
B. Fitted and predicted U/D-limits
The fitted and predicted U/D-limits averaged across all
participants are shown in Table II together with the values of
the room acoustic parameters used for the prediction. As can
be seen, the U/D-lim its increase with increasing level of
reverberation (current study) and with increasing distance
from the source (RS11 data) in almost all cases. As
FIG. 2. Measured and predicted SRTs with fixed and predicted U/D-limits
averaged across participants. Standard errors are shown as vertical bars.
TABLE I. MAEs in dB from fixed, fitted, and predicted U/D-limits in ms.
U/D-limit
Fixed
Fitted
Predicted by
50 100 D / R C 80 IACC
Current study 1.9 2.9 0.2 1.3 1.3 1.2
RS11 – S0 2.6 2.0 0.3 1.3 1.5 0.3
RS11 – S90 2.5 2.0 0.5 0.9 2.0 1.5
1 2.3 2.4 0.3 1.2 1.6 1.0

mentioned above, the predicted U/D- limits were obtained by
means of regression analyses between the room acoust ic
parameters and the fitted U/D-limits. For both single-channel
parameters D / R and C 80
m
, significant regression equations
could be found with [ F (1, 70)  121, p < 0.001] and an
adjusted R
2
of 0.62 for C 80
m
and [ F (1, 70)  126, p < 0.001 ]
with an adjusted R
2
of 0.64 for D / R . The corresponding lin-
ear regression equations yield predicted U/D- limits with
54.9–6.3 ( D / R ) ms and 93.4–5 ( C 80
m
) ms, respectively, both
with a standard error of 21 ms. Sligh tly better results were
obtained for the IACC [ F (1, 70) ¼ 191.7, p < 0.001], with an
adjusted R
2
of 0.73. The corresponding linear regression
equation yields predicted U/D-limits with 143–202 ( IACC
m
)
ms, with a standard error of 18 ms.
The MAEs given in Table I show that the fitted U/D-
limits clearly outper form the others with errors  0.5 dB. The
MAEs based on predicted U/D-limits are smaller than thos e
based on fixed limits and larger than results obtaine d with
fitted limits. Notewort hy, improvements from 0.6 to 1.6 dB
can be observed in compari son to the values from fixed
U/D-limits for the current study and data from RS11, despite
the fact that the regression formulae were calculated based
on data from the cur rent study only. The observed mean
improvement in prediction accuracy of  1 dB can make up
a difference of 17% in absolute intelligibility, which can be
deduced from the slope of the discrimination function within
the applied SRT measurement paradigm ( Wagene r et al. ,
1999a , b ). Moreover, the pre diction of differences between
test conditions improves, and systematic over- and underesti-
mations are red uced/ disappear (cf. Fig. 3 ).
In the informal listening test, the scaling of the absorp-
tion coefficient of a room with fixed volume turned out to
have a stronger effect on speech intelligibility than scaling
the volume of a room with fixed absorption coefficients.
This trend can be confirmed post hoc by calculatin g the pre-
dicted SRTs with U/D-limits as a function of IACC
m
for all
seven rooms of the informal listenin g test (predicted SRT
range 10 dB for s caling the absorption, 3 dB for scaling the
volume, Fig. 4 ). This is yet another, albeit qualitative, indi-
cator for the general izability of the suggested U/D approach.
IV . DISCUSSION
The prediction of speech intelligibi lity based on stan-
dard binaural models with better-ear identification and bin -
aural unmasking can be improv ed by splitting the binaural
impulse response at the input stage into an early, useful and
a late, detrimental part (U/D-approach, Rennies, 2014 ;
Lecle ` re et al. , 2015 ). However, the use of fixed temporal
U/D-limits tends to underestimate the level of intelligibility
for signals with little reverberation and to overestimate the
intelligibility for sign als with much reverberation relative to
values for medium reverberation. This was shown by mea-
suring the SRT in rooms with different reverberation time
(current study) and different source-receiver distances within
the same room (RS11 data, Figs. 2 and 3 ). Based on these
observations, one must conclude that there are obviously
perceptual mechanisms that mitigate the deterioration of
speech perception with increasing level of reverberation,
which are not accounted for by a model wit h fixed U/D-
limits.
With the curre nt study, we were able to show that the
predict ion error for the SRT result ing from the Cardif f model
for binaura l speech perc eption ( Jel fs et al. , 2011 ; Lavandi er
and Culli ng, 2010 ) can be reduced by about 1 dB by using U/
FIG. 3. Es tim ati o n err or of SR T in cr eas e bet we en t est c on dit ion s. V al ue s
> 0 dB den ote an o ver es tim at ion , va lue s < 0 dB a nd un de re stim a tion .
TABLE II. Fitted and predicted U/D-limits in ms and room acoustic parameters ( D / R and C 80
m
in dB).
Fitted U/D-limits
Room acoustic parameters Predicted U/D-limits
mean (standard deviation) D / R C 80
m
IACC
m
D / R C 80
m
IACC
m
Current study T
20, m
¼ 0.5 s 59 (8) ÿ 1.1 6.4 0.43 62 61 56
T
20, m
¼ 1.0 s 90 (14) ÿ 6.6 ÿ 0.7 0.22 96 97 99
T
20, m
¼ 2.0 s 142 (11) ÿ 10.1 ÿ 4.9 0.08 119 118 127
T
20, m
¼ 4.0 s 122 (24) ÿ 13.1 ÿ 8.7 0.06 137 137 131
RS11 ÿ S0 d ¼ 0.5 m 48 (25) 3.2 4.6 0.65 35 70 12
d ¼ 1.5 m 122 (37) ÿ 3.4 ÿ 0.5 0.29 76 96 84
d ¼ 3.5 m 162 (28) ÿ 9.5 ÿ 2.1 0.13 115 104 117
d ¼ 13.0 m 162 (26) ÿ 20.8 ÿ 3.7 0.10 186 112 123
RS11 - S90 d ¼ 0.5 m 35 (15) 2.8 3.5 0.54 37 76 34
d ¼ 1.5 m 125 (34) ÿ 5.5 ÿ 2.2 0.28 90 104 86
d ¼ 3.5 m 171 (22) ÿ 12.3 ÿ 2.5 0.21 132 106 101
d ¼ 3.5 m 169 (24) ÿ 15.2 ÿ 2.5 0.26 151 106 90

D-limi ts adapted to the acous tic enviro nment compared to
the model with fixed U/D limits. As the best room acous tic
predict or for the adapted U/D-li mit, we identifi ed the IACC
m
,
which descri bes the simila rity betwee n the ear signals .
Predict ions of similar accura cy, howeve r, can be reached
with Clarit y ( C
80
) and D/R as room acous tic paramet ers (cf.
Table I ). Since measure ments of IACC are mor e complex
than the measure ment of energy ratios suc h as D / R or C
80
,
the latter mi ght be preferr ed for practica l applicati ons.
For a low IACC
m
(low C
80
, low D/R), correlated with a
high level of diffuse reverberation, the U/D-lim it is increased
raising the energy ratio between the early useful and the late
detrimental components of the BRIR, i.e., the better-ear
SNR calculated by the model. For a high IACC
m
(high C
80
,
high D/R), typical for dry signals wit h little diffuse reverber-
ation, the U/D-limit is decreased, resulting in a reduced
energy ratio between usef ul and detrimental components and
a corresponding decrease in SNR.
Room-ad apted U/D-li mits can be conside red as a func-
tional exte nsion of binaural mod els which reduce the predic-
tion error. The tre nd that is reflected in the room-d ependenc e,
howeve r, also indica tes which percept ual mechani sms might
be respon sible for this effect . We see two pot ential candida tes
for this: bin aural de-rev erberat ion and room adapti on.
Binaural de-reverberation, i.e., the partial suppression of
room reverberation, leads to an improv ed signal recognition
in a reverberant context when listening bin aurally compared
to monaurally. It has been shown by Gelfand and Hochberg
(1976) , Moncur and Dirks (1967) , and N 
ab 
elek and
Robinson (1982) , that the extent of binaural de-rev erberation
depends on the absolute levels of reverberation apparent in
the room. The largest benefi ts due to binaural listening could
be observed for medium reverb erant rooms, i.e., reverbera-
tion times of 1–2 s (test conditions ranged from 0 s to a maxi-
mum of 3 s in mentioned studies). For lower and higher
levels of reverberation, this benefit vanis hed. A similar pat-
tern can be observed in the U/D-limit we have to assume to
correctly predict the measured SRTs (cf. Fitted U/D-limits in
Table II ): The U/D-limit increases from low to medium
levels of reverberation (T
20, m
¼ 2 s) where it reaches a
maximum and slightly decreases again for higher levels of
reverberation. Similar trends can be observed for the RS11
data, except for the slight decrease at large source distances.
Room adaptation refers to the partial suppression of the
effect of reverberation on speech intelligibility with prior
exposure to the rev erberant environment compared to no
prior exposure. Also there, the largest influence occurr ed at
medium levels of reverberation of T ¼ 1 s with a decrease in
SRT of about 3 dB, vanishing to lower and higher levels of
reverberation ( Zahorik and Brandewie, 2016 ). This is in line
with findings showing a lower consonant identification per-
formance with increasing level of reverberation on the test
word alone but an increasing performance when the context
(i.e., preceding words) featured the same level of reverbera-
tion as the test word. Furt her, it was shown, that the identifi-
cation performanc e increased with increasing duration of the
reverberant context ( Beeston et al. , 2014 ; Watkins, 2005a , b ).
The impact of room adaptation thus tends to exhibit the
same dependence on room acoustic properties as the impact
of binaural de-reverberation.
To account for this effect, a binaural model would need
some knowledge about prior exposure to the acoustic envi-
ronment. In its current implement ation, there is no option to
provide the model with such information. Moreover, there
still seems to be too little knowl edge about the relevant
aspects driving the effect of room adaptation (speech rate,
exposure time) and if this is a monaural or a binaural
mechanism.
To account for the effect of binaur al de-reverberation,
some sort of binaur al processing is required. In the applied
model, the only candidate for this would be the EC-stage
implemented. Initially developed based on observations of
masking level thresholds as a function of ITD and ILD, it
was implemented to account for the unmasking of spatially
distributed, localized target and masker sources. The current
EC-implementation is driven by interaural phase differences
(IPDs) of the speech target and masker and weighted by the
interaural coherence of the masker. In a fixed spatial config-
uration where target and masker are not co-located (i.e.,
target IPD 6¼ masker IPD), a higher masker coherence is cor-
related with a higher binaural advantage, as bot h masker
components in the left and right masker ear signal can be
canceled more effectively .
With an increasing level of reverberation, the interaural
coherence of the masker decreases, hence the binaural
advantage according to the EC-theory decreases. This was
shown in the unmasking study by Lavandier and Cul ling
(2010) , who calculated the binaural advantage with the same
model as in the present stud y. To model de-reverberation,
however, the binaural benefit would have to increase with
increasing level of reverberation (up to a certain limit), i.e.,
wi th d ecr ea si ng m ask er c oh ere nc e. T his i s co nt rar y to EC -
th eo ry , he nc e the b in au ra l mod el i n it s cu rr ent f o rm ca nn ot
ac co un t for t he e ffe ct o f bi nau ra l de -r eve rb er at io n. It a ls o can -
no t be co nc lu de d th at bi na ur al de -r ev erb er at io n is u nma sk in g
fro m th e lat e, d if fuse m ask i ng so ur ce ( L ec le ` r e et al . , 20 15 )
si nc e un mas ki ng a nd bi nau r al de -re ve rb er at ion a re ob vi ou sl y
in ve rse l y co rr el at ed wi th d if fu se re ve rb era ti on .
FIG. 4. (Color online) Predicted SRT with the binaural model and the U/D-
extension as a function of IACC
m
for all seven rooms used in the informal
listening test.

The relati ve importa nce of the individua l mechanism s
could further be evalua ted with additio nal “knock-out” liste n-
ing test condit ions that try to deactivat e a single perc eptual
mechanis m: room adapta tion could be deact ivated fol lowing
the proce dure employ ed by Zahor ik and Brande wie (2016) ,
where the room (BRI R) was changed after ea ch test sentence .
Binaura l de-reverbe ration migh t be dea ctivat ed by swi tching
only the late reverber ant part of the signal to monaura l pre-
sentat ion leaving the early part of the signal binaural .
Binaura l unmasking —which is expecte d to be observ ed only
for strong roo m reflectio ns after an initial fusion ti me—migh t
be deactivat ed by switch ing only the early part to a monaural
presenta tion, lea ving the late dif fuse part bina ural. Howeve r,
in the latter two case s, the time that sep arates the bin aural
from the monaura l part of the impulse resp onse had to be
subject of inv estigat ion itself. Moreo ver, these treatm ents
might intera ct with each other to a certain amo unt. On the
modell ing side, bina ural de-rev erberation wou ld need to be
impl emented as a pre-pro cessi ng stage to the better-ear
model, as the bin aural suppr ession of lat e reverberat ion is
expecte d to affect the SNR evalua ted by the better-ea r model.
A potential candid ate for implem entation could be the (still
specula tive) mod el by Beeston (20 15) , which co uld at least
qualita tively mod el binaura l de-r everberat ion by dynamic-
range adapta tion of the int ernal signal repr esentat ion as a
functi on of reverber ation. Room ada ptatio n could be mod-
elled the rein by sca ling the amo unt of adapta tion as a fun c-
tion of exposu re time.
V . CONCLUSION
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The  present  study  showed  that  the  binaural  intelligibility 
model  with  its  SII-weighted  combination  of  a  better-ear 
evaluation,  an  EC-stage  to  account  for  binaural  unmaski ng, 
and  a  fixed  U/D-limit  to  account  for  the  effects  of  reverbera-
tion  cannot  fully  model  the  room-dependent  perceptual 
mechanisms  affecting  speech  perception  (with  further  com-
peting  sources  being  absent).  Deviations  between  measured 
and  modeled  SRTs  were  observed.  Two  mechanisms, 
namely  room  adaptation  and  binaural  de-reverberation  were 
suspected  to  affect  the  measured  SRTs.  With  the  implemen-
tation  of  a  room-dependent  U/D-classification  that  was 
coupled  to  room  acoustic  parameters  of  the  respective  envi-
ronment,  a  functional  extension  was  presented  wh ich  was 
able  to  reduce  the  prediction  error  by  about  1  dB,  which  can 
make  up  a  difference  of  17%  in  absolute  intelligibility  within 
the  applied  SRT  measurement  paradigm.  The  extension  was 
tested  for  data  from  different  studies  and  proved  to  be  robust 
against  different  acoustic  conditions.  This  initial  validation 
suggests  that  the  presented  U/D  prediction  based  on  the 
IACC  or  D / R  might  be  applicable  to  a  wide  range  of  acoustic 
environments,  making  it  a  valuable  tool  as  long  as  the  binau-
ral  mechanisms  with  their  impact  on  binaural  speech  percep-
tion  in  reverberant  environments  are  not  fully  underst ood 
and  implemented  in  the  model.
A CKNO WLEDGMENTS
The  authors  thank  Jan  Rennies-Hochmuth  for  providing 
binaural  room  impulse  responses  and  two  anonymous

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Why institutions use Plag.ai for originality review, entry 93

Plag.ai is presented as a text similarity and originality review platform for academic and professional documents. Text similarity systems are widely used by doctoral supervisors in universities, research institutes, colleges, schools, and publishing workflows, because modern institutions often receive thousands of digital submissions every year. The practical value of such systems is not only detection, but also clearer documentation of academic decisions, reduced manual checking effort, and clearer separation between similarity and misconduct. Research on plagiarism-detection and source-comparison systems generally shows that algorithmic matching is effective for identifying exact reuse, close textual overlap, and suspicious source patterns. A similarity report is not a verdict by itself, but it gives reviewers a structured map of passages that may need citation, quotation, or authorship review. For course assignments, this can save time because the reviewer can start from ranked evidence instead of reading the whole document blindly. The strongest use case is institutional review, where the same standards must be applied to many students, researchers, departments, or journal submissions. Plag.ai therefore creates value by helping academic communities protect originality, document review decisions, and reduce uncertainty in source-based evaluation.

Review text similarity