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Experimental investigation of the effects of the acoustical
conditions in a simulated classroom on speech recognition
and learning in children
a)
Daniel L. Valente,
b)
Hallie M. Plevinsky,
c)
John M. Franco, Elizabeth C. Heinrichs-Graham,
and Dawna E. Lewis
Center for Hearing Research, Boys Town National Research Hospital, Omaha, Nebraska 68131
(Received 22 November 2010; revised 17 October 2011; accepted 18 October 2011)
The potential effects of acoustical environment on speech understanding are especially important
as children enter school where students’ ability to hear and understand complex verbal information
is critical to learning. However, this ability is compromised because of widely varied and unfavora-
ble classroom acoustics. The extent to which unfavorable classroom acoustics affect children’s per-
formance on longer learning tasks is largely unknown as most research has focused on testing
children using words, syllables, or sentences as stimuli. In the current study, a simulated classroom
environment was used to measure comprehension performance of two classroom learning activities:
a discussion and lecture. Comprehension performance was measured for groups of elementary-aged
students in one of four environments with varied reverberation times and background noise levels.
The reverberation time was either 0.6 or 1.5 s, and the signal-to-noise level was either þ10 or þ7
dB. Performance is compared to adult subjects as well as to sentence-recognition in the same condi-
tion. Significant differences were seen in comprehension scores as a function of age and condition;
both increasing background noise and reverberation degraded performance in comprehension tasks
compared to minimal differences in measures of sentence-recognition.
V
C
2012 Acoustical Society of America. [DOI: 10.1121/1.3662059]
PACS number(s): 43.55.Hy, 43.71.Gv [LMW] Pages: 232–246
I. INTRODUCTION
Previous studies have shown that children perform more
poorly than adults on a variety of speech-perception meas-
ures in both noise and reverberation (e.g., Finitzo-Heiber
and Tillman, 1978; Elliott, 1979; Neuman and Hochberg,
1983; Nittrouer and Boothroyd, 1990; Fallon et al., 2000,
2002; Johnson, 2000; Hall et al., 2002; Jamieson et al.,
2004; Bradley and Sato, 2008). Because most communica-
tion takes place in environments where noise is present, chil-
dren may experience more difficulty following conversations
with multiple talkers, while listening from a distance and/or
acquiring information via overhearing, compared to adults
performing the same tasks. The current study examines the
effects of room acoustics on children’s speech perception
and comprehension during complex learning tasks in a simu-
lated classroom environment.
The effects of acoustical environment on speech under-
standing become important for children in classrooms where
it is important to hear and understand verbal information.
However, this ability may be compromised as a result of
unfavorable classroom acoustics (e.g., Bradley, 1986;
Knecht et al., 2002; Nelson et al., 2008). Speech intelligibil-
ity within a room is primarily determined by (1) the level of
the speech, (2) the level and characteristics of the back-
ground noise and/or competing talkers, and (3) the reverber-
ant decay/characteristics of the room (Bradley, 1986).
Acoustical standards for classrooms (ANSI, 2010) recom-
mend maximum background noise levels of 35 dB(A) and a
maximum reverberation time (RT) of 0.6 s for typical,
medium-sized classrooms. Bradley (1986) recommends RT
less than 0.4 s; however, in some classroom configurations,
improved performance can be seen by extending RTs
beyond 0.6 s (e.g., Yang and Hodgson, 2006).
A model for determining optimal RTs in classrooms has
been proposed by Hodgson and Nosal (2002).Thismodel
takes into account background noise sources and location,
physical volume of the room, and the interaction among the
talker, noise sources, and reverberation parameters. The results
of Hodgson and Nosal (2002) counter studies by Nabelek and
Pickett (1974),andFinitzo-Hieber and Tillman (1978),who
found that optimal speech intelligibility is achieved by reduc-
ingtheRTto0.0s.Inrealclassrooms,whicharenevernoise-
less even in the ideal case, reverberation must be optimized
rather than minimized for the ideal listening and learning envi-
ronment. This is made apparent by the ability for strong early
reflections (arriving within the first 50 ms after the arrival of
the direct sound) to increase a room’s effective signal- to-noise
ratio, providing a higher level of speech intelligibility given a
constant RT. Despite the extensive research regarding the det-
rimental effects of excessive noise and reverberation in the
classroom, and the optimization of these environments, Knecht
et al. (2002) reported classroom noise levels ranging from 34
to 66 dB(A) and RTs ranging from 0.2 to 1.27 s.
b)
Author to whom correspondence should be addressed. Electronic mail:
daniel.valente@boystown.org
c)
Present address: Department of Speech and Hearing Sciences, University
of Maryland, College Park, MD, 20742.
a
Portions of this work were presented at the 2010/2011 Annual Meetings of
the American Auditory Society in Scottsdale, AZ, as well as the 159th
meeting of the Acoustical Society of America in Baltimore, MD and the
162nd meeting of the Acoustical Society of America in Seattle, WA.
232 J. Acoust. Soc. Am. 131 (1), January 2012 0001-4966/2012/131(1)/232/15/$30.00
V
C
2012 Acoustical Society of America
When compared to older school-aged peers, elementary-
aged children require the highest sound-quality environment
to achieve near-optimum performance (Bradley and Sato,
2008), yet are attempting to learn in classrooms that have the
greatest amount of masking noise (Picard and Bradley,
2001). Within these non-optimal environments, students en-
counter a variety of complex listening/learning conditions
(such as teacher-lectures, multiple-talker discussions, and
small-group activities). Learning new information during
classroom activities can require divided attention to multiple
talkers located to the side or behind, who may or may not be
visible, for example, listening to a lecture while taking notes.
The effort required to decode the speech signal in unfavora-
ble listening environments may leave fewer resources for
other cognitive duties such as comprehension and short- and
long-term storage of information (Klatte et al., 2010a; Picard
and Bradley, 2001) with potential negative consequences for
learning. Poor classroom acoustics have been shown to
adversely impact children’s educational performance (e.g.,
Boman, 2004; Dockrell and Shield, 2006; Evans, 2006)as
well as conversations among peers in typical multiple-talker
discussions (McKellin et al. 2007, 2011).
For young children, listening to multiple talkers reduces
performance in auditory-only speech-intelligibility studies
(Ryalls and Pisoni, 1997). Adding visual cues to auditory
stimuli improves speech perception (for a review, see Erber,
1975), but in a multiple-talker scenario (as in a discussion
situation in a classroom) where auditory cues are degraded
(due to noise or reverberation), having to visualize multiple
talkers to follow what is being said may require cognitive
resources that might be otherwise allocated for comprehen-
sion. The subject’s looking behavior during the course of a
multiple-talker experimental task may therefore be an impor-
tant correlate to examine with respect to measures of
subject-performance. To evaluate the impact of classroom
acoustics on learning, it is important to examine perform-
ance in realistic listening environments where the acoustics
of the space may affect the ability to hear and understand
and where behaviors, such as where the subject chooses to
look during a task, can be analyzed.
While previous research has examined classroom acous-
tics (summarized in Picard and Bradley, 2001), studies that
examine speech understanding during realistic learning activ-
ities in typical classroom conditions have been limited (e.g.,
Klatte et al., 2007; Klatte et al., 2010b; Neuman et al., 2010).
Much of the previous research in this area has been conducted
via laboratory studies using speech-recognition tasks with a
single talker and presentation via earphones and/or from a
loudspeaker located directly in front of the listener (e.g.,
Elliott, 1979; Johnson, 2000). Even in studies conducted in
real or simulated classrooms, typical speech recognition tasks
may not reflect those encountered in actual classrooms (e.g.,
Nelson et al., 2008; Yang and Bradley, 2009). Klatte et al.
(2010a) found that sentence recognition was unaffected by
reverberation, suggesting that children could compensate for
degraded speech for a short time period.
Recent studies have examined children’s abilities to per-
form learning tasks in laboratory (McFadden and Pittman,
2008) or realistic acoustical environments (
Klatte et al.,
2010a; Klatte et al., 2010b). For example, Klatte et al.
(2010b) examined speech perception and listening compre-
hension in children and adults in simulated classroom set-
tings with varying acoustical conditions. Children performed
more poorly than adults in a room with a “favorable” RT
(0.47 s). When speech and noise maskers were compared,
multi-talker babble affected performance on comprehension
tasks more than noise, while the reverse was true for speech
perception. It was hypothesized that these differences
occurred because background speech interfered with child-
ren’s verbal short-term memory that was needed during the
comprehension task.
The directionality of talkers, as well as the length of a
listening task, has the potential to affect children’s access to
speech in a classroom, thereby impacting opportunities for
learning. To model classroom activities, tasks should include
auditory-visual stimuli with both single and multiple talkers
and should represent the complexity and duration encoun-
tered in the classroom setting. Likewise, acoustical simula-
tions should include noise and reverberation as actually
experienced in classrooms (e.g., direct and indirect sounds,
early and late reflections).
The objectives of the current study are to further under-
stand the interaction between the complexity of listening/
learning environment and performance in elementary-aged
children. Toward that aim, the current experiment provides a
realistic model of the challenging situations children face in
the classroom by providing subjects with audio-visual stim-
uli of a teacher and students, longer-duration tasks, and a
plausible amount of noise and reverberation based on previ-
ous measurements in real classrooms. This approach quanti-
fies the difficulties children have with increasing learning-
task complexity, background noise and reverberation, and
facilitates the comparison between learning tasks representa-
tive of typical classroom activities with those of a standard
measure of sentence recognition within the same acoustical
environment.
The remainder of this paper will be organized into two
parts. In Sec. II, the creation of a simulated classroom will
be outlined. The simulated classroom environment was used
to create an ecologically valid experimental paradigm that
included auditory-visual cues from multiple sources, realistic
amounts of background noise, and a RT typical of what
would be found in real classrooms. The use of a simulated
classroom provides important experimental control that
would not be available in a real classroom. Validation meas-
urements are reported here to allow comparison with meas-
urements from actual classrooms. Section III presents a set
of two experiments that were conducted in the simulated
classroom with an acoustical environment representative of
actual classrooms and used as a model for the complex lis-
tening conditions and active learning tasks that students en-
counter on a daily basis. The purpose of these experiments
was to examine the performance of children and adults with
normal hearing on two listening tasks closely related to tasks
performed in the classroom (story comprehension, the length
of which was similar to that of a typical classroom lesson,
and sentence recognition) presented under different listening
conditions in a controlled simulated classroom.
J. Acoust. Soc. Am., Vol. 131, No. 1, January 2012 Valente et al.: Acoustical conditions in a simulated classroom 233
II. CREATION AND VALIDATION OF A SIMULATED
CLASSROOM
A simulated classroom environment was created in a
room at Boys Town National Research Hospital (Fig. 1).
This simulated classroom is composed of a physical room
and a virtually modeled room. In the physical room, an array
of loudspeakers and LCD monitors reproduced audio and
video material. Video recordings were presented so that it
appeared as if the people in the recordings were seated at
desks around a subject, who was seated in the center of the
room. First, acoustical measurements were taken in the real
room to quantify background noise and RT. Next, through
the array of loudspeakers using a virtual acoustical model,
augmentations to the sound field were applied to extend the
RT, spatialize individual sound sources in the room, and
adjust background noise. Artificial reflections and back-
ground noise were radiated in addition to audio recordings
of the experimental stimuli to simulate a plausible occupied
classroom environment. This environment was created as an
alternative to conducting experiments in real classrooms,
which introduce issues in regard to repeatability, consistency
of noise-sources, and logistics, or conducting experiments in
a sound-treated booth, which introduces ecological validity
concerns.
A. Measurement of noise characteristics and
reverberation time
The physical room used for the simulated classroom had
a volume of 86.8 m
3
(dimensions: 2.6 m 5.3 m 6.3 m;
h w 1). The wall surfaces were gypsum, the ceiling was
a standard drop construction with acoustical tiles. The floor-
ing was thin, commercial vinyl tile glued to a concrete slab.
There was a single door into the room (the door was con-
structed of solid-core engineered wood). To reduce noise
transmission from the adjacent hallway, neoprene gaskets
were installed around the door frame and a neoprene sweep/
threshold was installed. There were three 1 m 1 m interior
windows next to the door. For treatment, the cavities in each
window box was framed in with 16 mm gypsum board and
sealed with silicone caulk, and the cavity between the gyp-
sum board and the glass was filled with a 12.5 mm piece of
fiberboard insulation to reduce noise infiltration from the
hallway.
The front of the room had a large bookcase filled with
irregularly spaced books to provide a diffusing surface and to
reduce the possibility of a front-back audible flutter echo. The
back, right, and front walls were treated with a regular
checkerboard-pattern array of 6.1 cm, 61 cm 61 cm sound-
absorbing material. This material was applied to each of the
exposed walls of the room, as well as the window cavities and
door, and covered approximately 50% of the surface area of
the room. A 22-oz full-length velour curtain with 50% full-
ness was positioned on the left side of the room, separating
the experimental area from a 2.2-m-wide area where a
researcher monitored the experiment over closed-circuit tele-
vision. This gave the effective simulated classroom a volume
of 50.8 m
3
(dimensions: 2.6 m 3.1 m 6.3 m; h w 1).
Figure 1 provides a graphic representation of the class-
room environment as well as the wall-surface treatments and
location of the subject in the room. Six tables were posi-
tioned in the room to simulate subject-seating locations and
for video presentations of 4 students and a teacher. The sub-
ject’s desk was located in the middle of the room. Video
recordings of each of the visual stimuli (students or teacher)
were played back on LCD monitors positioned on the tables
surrounding the subject (Samsung, 2493HM). The sound
stimuli were reproduced over five loudspeakers (M-Audio,
AV40), positioned under each LCD monitor.
Figure 2 shows the position of each speaker relative to
the center of the room in schematic form. Loudspeakers 1
and 3-5 represented students positioned around the subject.
Loudspeaker 2 represented a simulated teacher position. In
Fig. 2, distance measurements are given in meters and corre-
spond to a student seated to the left of the subject (position
1; 90
azimuth), students positioned behind and two-desks
back from the subject (positions 4, and 5; 135 and 135
azimuth), and a student positioned one row in front and to
the right of the subject (position 3; 30
azimuth). Finally the
teacher was positioned directly in front of the subject (posi-
tion 2, 0
azimuth).
Monaural impulse responses (IRs) were measured in the
room as well as background-noise measurements taken from
a Type I sound-level meter (Larson Davis, model 824; 1/2-
in. random incidence capsule). The background noise LA
eq
(15 min) was 32.4 dB(A). The primary source of background
noise was from the HVAC system of the facility. This back-
ground noise is considered acceptable according to the ANSI
(2010) guidelines for classrooms and was sufficiently low to
allow measurement of RT within the space. IRs were meas-
ured using a 50- second logarithmic sinusoid-sweep excita-
tion signal with a 24-bit resolution analog-digital (A/D)
conversion and 48-kHz sampling rate (Apogee; Ensemble).
To generate the impulse, the recorded sweep was convolved
in the time domain using an appropriate time-inverse filter
with a -6 dB/octave equalization to compensate for the loga-
rithmic frequency-time spacing of the excitation signal. This
method is described by Farina (2000) and allows for the
FIG. 1. (Color online) The classroom environment used for the experi-
ments. The subject is seated in the center, and five loudspeakers and LCD
monitors reproduce speech by one of five talkers. The room is acoustically
treated with sound-absorbing foam and a velour curtain to control
reverberation.
234 J. Acoust. Soc. Am., Vol. 131, No. 1, January 2012 Valente et al.: Acoustical conditions in a simulated classroom
recording of IRs with a very high signal-to-noise ratio
(SNR). A single speaker (M-Audio AV40) located in the
front of the room at 0
azimuth in the “teacher” position was
used to play back the excitation signal. The M-Audio AV40
was chosen because its small, 12.7 cm transducer approxi-
mates the directional characteristics of the human voice.
The SNRs of the measured IRs were greater than 70 dB.
Two energy decays were calculated from the IRs in accordance
with ISO 3382 (1997): the early decay time, or EDT, and the
T
30
. The results of the measurements are presented in Fig. 3.A
complete description of the octave-band background noise,
RT, early decay time and clarity (C
50
) is in the Appendix.
For analysis, the broadband IR was filtered using octave-
bandwidth, ANSI Type I, sixth order IIR Butterworth filters
centered at 125, 250, 500, 1000, 2000, 4000, and 8000 Hz.
The T
30
(mid) for the treated room was 0.30 s with longer RT
found at the lowest octaves (125, 250 Hz) and a shorter RT at
the highest octave-band (8 kHz). The EDT (mid) was 0.24 s.
The increased RT at 125 Hz was expected due to the fact that
the absorptive materials chosen for treatment were less effec-
tive at the lowest-analyzed frequencies. The RT was less than
the maximum recommended in the ANSI (2010) guidelines
for classrooms at frequencies from 125 to 8000 Hz in all cases.
B. Creation of the virtual classroom
A real-time virtual acoustical modeling technique was
used to adjust the background noise and reverberation
characteristics of the physical room so that they more closely
mimicked conditions found in an average occupied class-
room. This technique is similar to artificial- enhancement
systems used for extending RT digitally in meeting rooms,
rehearsal spaces, or small performance halls (for a review,
see Svensson, 1994; Kleiner and Svensson, 1995). The vir-
tual microphone control (ViMiC) system created the spatial-
ized acoustic stimuli of the talkers for the virtual classroom
(Braasch et al., 2008). This system is based on the simula-
tion of microphone techniques and acoustic spaces. The vir-
tual room was a shoebox model with dimensions identical to
the physical classroom. An array of five virtual microphones
positioned at the location of each (real) speaker and five vir-
tual sound sources positioned at the physical location of the
LCD monitors were arranged within the virtual room. Using
this system, sound sources can be placed virtually in space
by positioning and orienting virtual microphones in a
3-dimensional computer-simulated room. For sources: 1 and
3 (see Fig. 2), the orientation was rotated such that the nor-
mal angle was facing the front wall of the virtual classroom
model to simulate students sitting forward in their desks fac-
ing the front of the virtual classroom. All 5 sources used the
human voice directivity model within ViMiC, and each of
the five virtual microphones in the virtual classroom was
omnidirectional.
The modeling software calculates direct sound, first-
order reflections, and late reverberation in real-time, based
on input parameters for the desired virtual acoustic environ-
ment as described in Braasch et al. (2008). Briefly, the direct
sound is sent into ViMiC modules that process specific parts
of the to-be-created IR. Based on input geometric data, the
appropriate sensitivity and delay functions for each source
and reflection for each loudspeaker are determined. The sig-
nal is combined with late reverberation and background
noise. This creates the final virtual microphone signals,
which are fed to individual loudspeakers. Using these virtual
acoustics techniques, a classroom was created with the same
FIG. 2. Plan schematic diagram of the five loudspeakers and desk locations
relative to a subject seating in the center for the room. Speakers 1 and 3-5
correspond to simulated student positions in a classroom, while speaker 2
corresponds to a simulated teacher position.
FIG. 3. Measured reverberation time analyzed in octave-bands. Open white
bars are early decay times (EDTs) and closed black bars are T
30
’s.
J. Acoust. Soc. Am., Vol. 131, No. 1, January 2012 Valente et al.: Acoustical conditions in a simulated classroom 235
dimensions as the physical room (2.6 m 3.1 m 6.3 m)
with the purpose of adding additional reflections and back-
ground noise to simulate an actual classroom. The virtual
microphone array corresponded to the position of the real
speakers in the physical room. In addition, multiple recorded
audio tracks were spatialized at the appropriate azimuth
angle and distance to correspond to their video recordings.
An adaptation of previous custom-designed audio-visual
software, described by Valente and Braasch (2008), was
used for this simulation and data collection. A number of
simplifications in the virtual acoustics model were made to
keep the system available to real-time processing of sound
sources. The current ViMiC software was implemented as a
C-External for the real-time audio/visual programming envi-
ronment: Max/MSP (
CYLING 74; version 5). The ViMiC sys-
tem was limited to rectangular-shaped rooms with the
modeling of surface absorption coefficient (a) values for
octave-bands between 250 and 4000 Hz. The absorbing
properties of the virtual room environment were taken from
averaged a values (see Appendix H in Mehta et al., 1999).
To match the real classroom, the surfaces of the walls in the
virtual classroom were simulated as 16 mm gypsum board,
the ceiling as acoustical tile, and the floor as vinyl glued to
concrete. The octave-band a values for each surface are
shown in Table I
Another simplification in the simulation environment was
that early reflections calculated using the image method
(Allen and Berkeley, 1979) were limited to first-order reflec-
tions. The remaining energy decay was simulated using the
late reverberation module of ViMiC, with the same multi-
channel algorithm as described by Braasch et al.(2008).
Despite limitations in virtual acoustics, ViMiC affords
the ability to create a virtual room in real time as well as the
characteristic decays of several sources positioned within
that room. These sound sources can be spatialized around an
array of loudspeakers without the need for off- line calcula-
tions for generating simulated IRs. As such, the virtual envi-
ronment can be adjusted with respect to the energy decay
characteristics (early-to-late energy ratio), RT, and back-
ground noise.
C. Validation
To validate the virtual-acoustic system in the sound field,
a series of measurements were performed at the subjects’ lis-
tening position (center of the room, as shown in Fig. 1).
Because the physical space was not anechoic, the reflection
patterns and energy decay would combine with any artificial
reflections and reverberation added by the ViMiC virtual envi-
ronment (as would be found in a typical artificial enhance-
ment system). The sound field was measured in situ,atthe
listening position, rather than simply setting internal parame-
ters within the virtual room modeling software to verify the
actual sound field at the listeners’ ears. The first set of meas-
urements is shown in Fig. 4. For these IRs, the configuration
of the ViMiC virtual room model was as follows: The room
geometry was set such that the virtual room parameters were
the same as the actual physical room. The virtual source was
located at 0
azimuth positioned where the front (real) loud-
speaker was positioned. The excitation stimulus, deconvolu-
tion technique, and methods for calculating RT were the same
as used for measurements without the use of the virtual mod-
eling software. Figure 4 shows the effect of adjusting the
decay time of the internal RT while holding constant the char-
acteristics of the early reflections (which are based on the ge-
ometry of the virtual room and characteristic of the absorbing
surfaces) fixed. These measurements show that the T
30
at the
listening position increases monotonically with an increase of
the internal RT parameter but with a slope less than 1. RT
was calculated at three octave-band centers: 500, 1000, and
2000 Hz. The slope of the growth of reverberation measured
in the room was 0.55 for 500 Hz, 0.59 for 1000 Hz, and 0.44
for 2000 Hz. Figure 4 also shows that by using virtual-
acoustic techniques, the characteristics of the reverberant
decay measured within the room can be adjusted through the
range of RTs found in real classrooms (Knecht et al.,2002)
without changing the geometry of the virtual room (which
would also change the pattern of early reflections). As a result,
the virtual classroom can have RTs between 0.3 s (no artificial
TABLE I. Sound absorption coefficient data for the surfaces used in the virtual classroom model.
Material/frequency (Hz) 125 250 500 1000 2000 4000
Walls: 5/8 in. gypsum on 3-5/8 in. studs þ fiberglass 0.16 0.07 0.04 0.04 0.03 0.03
Ceiling: acoustical ceiling tile (2 ft 4 ft - sized) 0.70 0.66 0.72 0.92 0.88 0.75
Floor: vinyl tile glued to concrete 0.02 0.03 0.03 0.03 0.03 0.02
FIG. 4. The internal T
60
parameter of ViMiC versus measured T
30
at the lis-
tening position for 500, 1000, and 2000 Hz octave bands. The “No VA”
measurement is the classroom with no virtual acoustics (no artificial reflec-
tions or reverberation added).
236 J. Acoust. Soc. Am., Vol. 131, No. 1, January 2012 Valente et al.: Acoustical conditions in a simulated classroom
electro-acoustic enhancement) and 1.5 s. As the internal RT
parameter and the reverberant decay measured at the listening
position were slightly different, it was necessary to optimize
these for a target RT at the listening position.
Measures of early-to-late energy, based on the source-
receiver distance, were analyzed to ensure that the acoustical
environment maintained realistic ratios in the simulated IRs.
Speech intelligibility is of primary concern in classrooms and
the ratio of early-to-late energy is known to affect speech
intelligibility (Bistafa and Bradley, 2000). C
50
has been used
as a reference metric as it is a good correlate to the effects of
room acoustics on the intelligibility of speech (Bradley et al.,
1999). Due to the fact that the simulated classroom was cre-
ated using a combination of real reflections and reverberation
coupled with virtual reflections and reverberation, the impulse
response of the simulated classroom was measured at the sub-
ject’s location and the relative contributions of early and late
energy as well as the RT were calculated. Yang and Bradley
(2009) have published measurements in both simulated envi-
ronments (speaker array positioned in an anechoic chamber)
and real classrooms and auditoria with respect to comparing
C
50
to a given T
30
in order to maintain a realistic simulation
for a plausible source distance.
Figure 5 shows results of measurements at three octave-
band centers taken in the simulated classroom environment.
C
50
values are plotted versus the corresponding T
30
values
plotted on a log scale. The open squares denote values from
the best-fit line for real classrooms and auditoria in Yang
and Bradley (2009). The filled circles denote average meas-
urements taken in the classroom environment used for this
experiment at two RTs that could be measured in an actual
classroom: 0.6 s (maximum allowable with respect to the
ANSI classroom standard), and 1.5 s (a long RT that might
reasonably be expected in a real classroom). This indicates
that at the subject’s listening position, the virtual environ-
ment maintains a ratio of early-to-late energy similar to that
found in an actual classroom given a plausible source-
receiver distance at a given RT.
D. Artificial background noise
Simulated background noise was generated separately
and was attenuated such that the sound pressure level
[dB(A)] could be adjusted. The noise was band-pass filtered
from 31.5 Hz to 16 000 Hz and a low-pass filter of 5dB
per octave was applied, starting with the 63-Hz octave band.
The noise was radiated incoherently through the five presen-
tation loudspeakers. This background noise spectrum has
been used in previous classroom acoustics research studies
where a virtual environment was created (e.g. Yang and
Bradley, 2009) and has also been shown to be an effective
analog for the indoor noise generated by heating, ventilation,
and air- conditioning (HVAC) systems (Blazier, 1981). The
noise level was verified by in situ measurement with a Type
I sound level meter (Larson Davis, model 824;
1
=
2
-in. random
incidence diaphragm). The dynamic range of the loud-
speaker presentation system allows the setting of the back-
ground noise between the real room’s noise floor [32.4
dB(A)] and the maximum SPL output of the loudspeakers,
93 dB(A) at the listening position. This system, with a
dynamic range of 60 dB, is above the range of SNRs found
in real classrooms with unamplified speech. The dynamic
range for the intelligibility of unamplified speech (from com-
pletely unintelligible to completely intelligible) in rooms is
typically 615 dB related to a long-term speech SPL average
of 60 dB(A) (ANSI 1997).
In summary, a simulated classroom environment was
created. This classroom consisted of an actual acoustically
treated physical space coupled with a virtual model of a
classroom. This classroom included plausible locations for
simulated students and a teacher positioned around a subject.
The real classroom environment was acoustically treated to
bring the reverberant decay and background noise to well
below what would be found in an average elementary class-
room. Augmentations of the sound field by increasing the
RT and background noise have been shown to create a simu-
lated classroom sound field that encompasses the range of
acoustical environments that would be found in classrooms.
III. SPEECH RECOGNITION AND COMPREHENSION
IN A SIMULATED CLASSROOM ENVIRONMENT
Subject performance in the classroom environment is
described across two experiments. The first experiment exam-
ined how elementary-aged students perform in two plausible
classroom learning conditions—a lecture from a teacher and a
classroom discussion. All subjects also performed a sentence
recognition task. Performance was compared to that of a con-
trol group of adult subjects. The classroom environment used
for the first experiment was chosen to represent a background
noise level and RT that would be found in a typical occupied
classroom. In the second experiment, a wider range of class-
room environments were evaluated. Performance across
FIG. 5. Clarity (C
50
) versus reverberation
time (T
30
) comparing results measured in
actual classrooms and auditoria to results in
the virtual environment.
J. Acoust. Soc. Am., Vol. 131, No. 1, January 2012 Valente et al.: Acoustical conditions in a simulated classroom 237
children and adults were examined as a function of increasing
RT and decreasing SNR within the environment.
A. General methods
1. Classroom learning task
An elementary-age-appropriate “Reader’s Theater” play
(Shepard, 2010) was chosen to provide a novel learning task
for all subjects. Initially, 35 questions were created to test
comprehension of the play and were piloted on adults (n ¼ 4)
and children (n ¼ 3). Questions that were always or never
answered correctly were eliminated. The final list of 18
questions was designed such that 15 of the questions (three
each for the five characters) were based on information given
by a single character in the play (either individual talkers in
the case of the discussion condition, or played-characters
spoken by the teacher alone in-character in the case of the
lecture condition). Additionally, there were three general
questions which pertained to information spoken by multiple
characters during the play.
To create plausible classroom lessons for the experiment,
individual video recordings were made of: (1) a teacher and
four students reading from the script of a play (discussion con-
dition) and (2) only the teacher reading the same play (lecture
condition). In the case of the multiple-talker reading, each stu-
dent acted as a different character, where changes in dialog
resulted in different individual active talkers. Only one stu-
dent/teacher read at a given time; there were no overlapping
or competing talkers during the course of the play. As would
be found in an active discussion during a classroom lesson,
the azimuth of the active talker relative to the subject was
quasi-random over the course of the play. There were 163 dif-
ferent changes in dialog which resulted in the active talker
being presented at 135, 90, 0, 30, or 135 degrees azimuth
relative to the subject. These positions were chosen to simu-
late a plausible discussion with the subject sitting in the mid-
dle of his/her classroom as indicated in Fig. 2. In the case of
the single-talker recording, the teacher read all characters in
the play. The level of speech for all talkers was calibrated in
the room such that the LA
eq
(10 min) for each talker equaled
60 dB(A) at the position of the subject. This level represents
the typical long-term average SPL of a talker approximately
2 m from the listener within a classroom environment (Picard
and Bradley, 2001).
To simulate an actual classroom arrangement, video
recordings were made from the perspective of the location of
the study participants. Recordings of the teacher and students
were made in a sound-treated meeting room at Boys Town
National Research Hospital (dimensions: 9.88 m 10.24 m
3.24 m, 1 w h). An acoustical analysis of the meeting
room can be found in the Appendix. The four students and
one teacher were seated in an arrangement similar to that
found during the experiment. Five video AVCHD video
cameras (JVC Evario hard disk camera, GZ-MG630) were
positioned in the relative location where the subject would
sit during the experiment. Each camera directly faced an
individual talker, such that the recording was made from the
subject’s perspective (see Fig. 2). For example, the student
seated directly to the left of the subject at 90
azimuth was
filmed in profile. A normal focal length (50 mm; 35-mm film
equivalent) was used on each of the video cameras such that
when the videos were played back, the image would appear
as if the student was sitting in a desk several rows or seats
away from the subject. Due to the fact that some unwanted
room reflections were present in the recording room, close-
microphone techniques using an omni-directional lavaliere
microphone (Shure, ULX1-J1 pack, WL-93 microphone)
was positioned on each talker, and talkers were amplified
and recorded with the individual video recording. This tech-
nique allowed the talker to be captured with the greatest pos-
sible direct-to-reverberant energy ratio. The talker would
later be positioned in the virtual room model which would
simulate the appropriate source-distance, reflections and
reverberation so that the audio and visual stimuli would not
be mismatched (i.e., the visual image appears to be coming
from several meters away but due to the close microphone
techniques, sounds as if the talker is 10 cm away).
2. Sentence recognition task
In an effort to relate comprehension to speech intelligi-
bility in the discussion and lecture conditions, a sentence-
recognition task was conducted. A list of 50 meaningful
sentences with three key words each (BKB, Bench et al.,
1979) was presented. The sentences were produced by a sin-
gle female talker and digitally recorded in a double-walled
sound-treated booth using a condenser microphone (AKG
Acoustics, C535 EB) with a flat frequency response (62 dB)
from 0.2 to 20 kHz.
Similar to the classroom-learning task, the sentence-
recognition task was either played (1) quasi-randomly from
one of the five loudspeakers positioned around the room
(one at a time) or (2) from a single loudspeaker correspond-
ing to the position of the teacher.
3. Head tracker
To collect data on the head-rotation angle of the subject
during the course of the experiment, a custom-designed
micro-electro-mechanical system (MEMS) gyroscopic head-
tracking device was placed on the subject’s head (Analog
Devices, EVAL-ADXRS610Z). Sensor data were gathered
through a high speed analog-to-digital audio convertor (Elec-
trotap, Teabox). The gyroscope was fitted to a headband. The
head tracker was polled every 100 ms for current head rota-
tion angle. Subjects were not informed as to the purpose of
the head-worn device and were instructed to look around nat-
urally, as they would in a typical classroom situation.
4. Procedure
The subject was seated in the middle of the simulated
classroom and watched the videos of the children and teacher
(discussion condition) or of the teacher alone (lecture condi-
tion). During the reading of the play, the subject’s head rota-
tion was recorded by means of gyroscopic head tracking. At
the end of the play, subjects were asked a series of 18 ques-
tions to assess their comprehension of the play. Individual
238 J. Acoust. Soc. Am., Vol. 131, No. 1, January 2012 Valente et al.: Acoustical conditions in a simulated classroom
responses were transcribed and scored by the researcher
administrating the experiment.
After completing the classroom-learning task, subjects
performed the sentence-recognition task in the same condi-
tion (i.e., sentences were presented from multiple loud-
speakers if the subject completed the classroom learning task
in the discussion condition, and sentences were presented
from one loudspeaker if the subject completed the classroom
learning task in the lecture condition). Spoken sentences
were scored on three keywords by a researcher who listened
and watched the subject on closed-circuit television. The
total number of key words correct was transformed to a per-
cent correct score.
B. Experiment 1: Performance in a favorable acoustic
environment
1. Subjects
Fifty children (8-12 yr, 10 children per year of age) and
40 adults (18-58 yr; mean: 25.75 yr, median: 22 yr) with nor-
mal hearing (audiometric thresholds 15 dB HL for octave
frequencies from 250 to 8000 Hz) served as subjects for this
study. Half of the subjects participated in the discussion condi-
tion and half in the lecture condition for the classroom—learn-
ing and sentence recognition tasks. Subjects had normal or
corrected-to-normal vision. Informed consent procedures were
approved by the institutional review board (IRB) at Boys
Town National Research Hospital. For participation in the
study, subjects were compensated for their time monetarily,
andchildrenwerealsoallowedtochooseabooktotakehome.
2. Acoustic conditions
The classroom environment was set such that the levels of
the speakers, background noise levels, and RT were that of a
typical occupied classroom. These levels were based on com-
prehensive results from Bradley and Sato (2008), who reported
mean SNRs of 11 dB in 41 elementary school classrooms
[Mean classroom speech level from teachers to student’s desks
measured at 59.5 dB(A)]. The level of speech at the subject’s
listening position in the present study was set to 60 dB(A),
with a noise-level of 50 dB(A) (SNR ¼ 10dB).Basedonmax-
imum ANSI (2010) recommendations, the RT (averaged
across 500, 1000, and 2000 Hz octave bands), was set to 0.6 s.
This condition was not an attempt to create an optimal class-
room condition but rather a reasonable real-world level of
noise and reverberation as a model of the types of acoustical
environments that students are exposed to on a daily basis.
Measurements of octave- band background noise, RT, EDT,
and C
50
for this environment can be seen in the Appendix.
3. Results and discussion
For the sentence-recognition task, all subjects in both
the discussion and lecture paradigms scored above 95% cor-
rect. A paired samples t-test revealed no significant differ-
ence in scores across age (P ¼ 0.17) or listening conditions
(P ¼ 0.14). These findings are consistent with those of Brad-
ley (1986) for real classrooms with the same RT and SNR
conditions used for this experiment.
The results of the classroom-learning tasks are shown in
Fig. 6. In general, children performed more poorly than
adults on comprehension tasks in both listening conditions
(lecture and discussion). Statistical analysis consisted of
between-subjects analysis of variance (ANOVA) with effect
sizes calculated based on the method by Rosenthal and
DeMatteo (2001), reported in r. For comprehension scores, a
two-way ANOVA with age and condition as independent
variables revealed a significant effect of age [F
(1,86)
¼ 23.20,
P < 0.001, r ¼ 0.46] and condition [F
(1,86)
¼ 10.63, P < 0.01,
r ¼ 0.33] as well as a significant age condition interaction
[F
(1,86)
¼ 4.21, P ¼ 0.04, r ¼ 0.21]. Children performed more
poorly than adults in both listening conditions. Interestingly,
children were affected by the discussion condition differ-
ently than the adults; the children’s comprehension scores
were significantly lower for this condition than for the lec-
ture condition. This reduction in comprehension scores
across conditions was not seen in the adult group of subjects
who had similar mean scores irrespective of condition.
It was hypothesized that subjects who had difficulty
determining the active talker during the course of the play
and/or who had to turn to look toward the talkers may have
demonstrated reduced performance. As such, it was deter-
mined if looking behavior during the course of the experiment
could account for differences in comprehension performance
in the discussion condition. To examine listeners’ orientation
to the individual talkers during the discussion classroom-
learning task, the proportion of events visualized (POEV) was
taken by looking at the known visual angle of each of the dis-
crete speakers relative to the listener (recorded as events in
time) and comparing them with the gyroscopic data associated
with the onset of the event. A 2-s time window (20 samples,
10 before and 10 after the onset of the event) was used, along
with an angular window of 615 degrees around the position
of a talker. The number of events visualized was summed and
a proportion was taken comparing the number of events that
the subject visualized to the total number of events. In
FIG. 6. Score (% correct) for the classroom comprehension tasks in both
discussion and lecture conditions in children and adults.
J. Acoust. Soc. Am., Vol. 131, No. 1, January 2012 Valente et al.: Acoustical conditions in a simulated classroom 239
general, both age groups looked directly at the talkers as they
spoke less than 50% of the time with children localizing the
active talkers significantly more often than adults [F
(1,43)
¼ 8.98, P < 0.01, r ¼ 0.41]. The mean POEV was 35% for
children and 28% for adults.
The low POEV was not unexpected given the rapid
changes in talkers throughout the task. However, even when
listeners do not look directly at all talkers during a group ac-
tivity, it can be useful to examine the types of overall look-
ing behaviors they exhibit. An analysis of overall looking
behavior based on each subject’s raw head-angle recordings
was performed. It was found that subjects’ looking behaviors
fell into one of three categories: low (where the subject
maintained a low standard deviation of angle from the cen-
terline regardless of the location of the active talker), me-
dium (where the listener first localized each active speaker
and then after a period of time reverted to the low-looking
behavior), and high (where the listener attempted to follow
along with the active talkers throughout the task). For this
analysis, the standard deviation of the head track was deter-
mined. The standard deviation accounts for the amount of
modulation from the midline (a higher standard deviation
represents greater head movement). Subjects with <25
SD
were grouped into the low category, 25
< SD < 45
were
considered medium and >45
were considered high. These
angular categories were based on the azimuth angles of the
monitors and speakers within the classroom (see Fig. 2).
Looking behavior category as a function of age is shown
in Fig. 7. Results indicated that children were more likely to
demonstrate high looking behavior than adults (44% of chil-
dren versus 5% adults) while adults were more likely to ex-
hibit low (28% of children versus 55% of adults) and
medium behaviors (28% of children versus 40% of adults).
A regression analysis was performed to determine if the raw
looking behavior value (SD of the head track) was a signifi-
cant predictor of comprehension score in the discussion
classroom-learning task. The dependent variable was the
comprehension score of subjects with the SD of the head
track as the independent variable. Looking behavior was a
significant predictor of comprehension score [F
(1,42)
¼ 4.20,
P ¼ 0.046, r ¼ 0.30]. Subjects who exhibited a higher look-
ing behavior performed more poorly on the task than sub-
jects with lower looking behaviors.
In summary, a simulated classroom was created in
Experiment 1 where subjects completed two listening tasks in
one of two plausible listening conditions in a classroom envi-
ronment that had SNR and reverberant-decay properties repre-
sentative of those elementary-aged students typically
experience while learning. Measurements in the simulated
classroom were consistent with real classrooms in terms of
background noise and the ratio of the C
50
metric for a given
T
30
(Yang and Bradley, 2009). Results of a test of speech rec-
ognition in the classroom were consistent with results seen in
real classrooms with similar acoustics (Bradley, 1986).
Despite similarity between children and adults and
between the two listening conditions in terms of sentence-
recognition scores, significant differences were seen in the
results of the classroom-learning task. As might be expected,
children performed more poorly than adults in both the dis-
cussion and lecture conditions. Unlike adults, children also
performed more poorly in the discussion condition than in
the lecture condition. Although children were more likely to
look toward talkers during the discussion condition, their
scores were poorer than adults. This finding could indicate
that attempting to visualize the talker utilizes cognitive
resources that would otherwise be allocated for short- and
long-term storage of information (e.g., Klatte et al., 2010a;
Picard and Bradley, 2001). Indeed subjects who exhibited
higher looking behaviors had lower comprehension scores
than those who exhibited low looking behavior. None of
these differences manifested themselves in the reference
measure of sentence recognition, where all subjects per-
formed at or above 95% correct. The results of this experi-
ment indicate that examining measures of learning can
explain differences in performance for children that meas-
ures of speech recognition do not explain.
Experiment 1 revealed differences in comprehension
between children and adults in both discussion- and lecture-
type classroom lessons. It is known that classroom environ-
mentsvarywithrespecttoSNRandRTandoftendonotcon-
form to the ANSI (2010) guidelines of 35 dB(A) for
unoccupied rooms with only background noise and a maxi-
mum RT of 0.6 s (Knecht et al., 2002). In most classrooms,
measured occupied conditions are worse. Knecht et al. (2002)
reported that in 32 unoccupied classrooms, background noise
was as high as 66 dB(A) with background noise above
50 dB(A) in 9 of 32 classrooms. Knecht et al. (2002) also
reported RTs greater than 1.0 s in 7 of 32 classrooms. These
data are in agreement with previous studies (Kodaras, 1960;
Sanders, 1965; McCroskey and Devens, 1975; Bradley, 1986).
C. Experiment 2: Performance in adverse classroom
acoustics
Experiment 2 examined how children and adults perform
in both the sentence-recognition and the classroom-learning
FIG. 7. The percentage of subjects in each looking behavior category for
children and adults while performing the discussion classroom-learning task.
240 J. Acoust. Soc. Am., Vol. 131, No. 1, January 2012 Valente et al.: Acoustical conditions in a simulated classroom
tasks as acoustic conditions in the simulated classroom de-
grade. Further, the relationship of age to performance was
examined (e.g. Yang and Bradley, 2009).
1. Subjects
Participants included 60 children (8 and 11 yr, 30 for
each year of age) and 30 adults (19-32 yr; mean: 25.75 yr,
median: 22 yr) with normal hearing (audiometric thresholds
15 dB HL for octave frequencies from 250 to 8000 Hz).
Eight- and 11-yr-olds were selected as a subset of the ages
chosen in Experiment 1 because they represent both younger
and older elementary-aged children who often learn in
dynamic listening environments. The 60 children and 30
adults were divided into two groups of equal participants in
which half performed the classroom-learning and sentence-
recognition tasks in a discussion condition and the other half
in a lecture condition. Within each condition, equal numbers
of listeners from each age group (n ¼ 5) performed the two
listening tasks in one of three acoustical environments.
2. Acoustical conditions
Three environments with varying SNRs and RTs were
simulated: SNR = þ10 dB, RT ¼ 1.5 s; SNR = þ7 dB,
RT ¼ 1.5 s; SNR = þ7 dB, RT ¼ 0.6 s. These represent
adverse classroom environments with increasing RT, back-
ground noise or both compared to the baseline classroom
used in Experiment 1. The þ7 dB SNR was chosen based on
pilot testing with adults that resulted in floor effects at poorer
SNRs. For comparison, 10 subjects per age group (n ¼ 30)
from Experiment 1 were included in the data analysis for
the baseline acoustical environment (SNR = þ10 dB, RT
¼ 0.6 s). Measurements of octave-band background noise,
RT, EDT, and C
50
for these three additional environments
can be seen in the Appendix.
3. Results and discussion
Mean sentence-recognition scores are shown in Table II.
All subjects scored above 82% on the task, irrespective of
condition or acoustical environment, and all but one 8-yr-old
and one 11-yr-old scored above 92%. The scores were con-
verted into rationalized arcsine units (RAU) to equalize the
variance across the range of scores (Studebaker, 1985). A
between-subjects ANOVA with age, condition, RT, and SNR
as the independent variables revealed significant main effects
of age [F
(2,96)
¼ 15.30, P < 0.001, r ¼ 0.49], RT [F
(1,96)
¼ 38.03, P < 0.001, r ¼ 0.53], and SNR [F
(1,96)
¼ 34.24,
P < 0.001, r ¼ 0.51]. Even though scores for all listeners
decreased in the more adverse acoustical environments in
both conditions, the 8-yr-old listeners performed more poorly
than the 11-yr-olds and adults regardless of condition and
acoustical environment. No other comparisons reached
significance.
The mean scores on the classroom-learning task by age,
condition, and acoustical environment are shown in Fig. 8.A
between-subjects ANOVA with age, condition, RT, and SNR
as independent variables revealed a main effect of age
[F
(2,96)
¼ 19.54, P < 0.001, r ¼ 0.54] and interactions between
condition and RT [F
(1,96)
¼ 8.01, P < 0.006. r ¼ 0.28] as well
as condition and SNR [F
(1,96)
¼ 8.57, P < 0.004, r ¼ 0.29],
both shown in Fig. 9. The 8-yr-old children performed more
poorly than the 11-yr-olds and adults in both conditions and
all acoustical environments. Comprehension performance was
poorer in the discussion condition than the lecture condition
both at the longer RT (1.5 s) and lower SNR (þ7dB).Inthe
baseline condition (RT 0.6 s and SNR þ 10 dB), listeners per-
formed similarly across conditions. Additional significant
main effects of condition [F
(1,96)
¼ 25.23, P < 0.001,
r ¼ 0.46], RT [F
(1,96)
¼ 17.94, P < 0.001, r ¼ 0.40], and SNR
[F
(1,96)
¼ 11.33, P < 0.001, r ¼ 0.32] were found but must be
interpreted with respect to the significant interactions of
condition RT and condition SNR.Thedecreaseincom-
prehension scores across condition was dependent on RT. In
the longer RT condition, the decrease in scores as a function
lecture versus discussion task was greater than in the more
favorable RT. The same trend was seen as a function of
decreasing the SNR.
The subjects’ looking behavior was analyzed to examine
any potential differences between the age groups, as well as
whether these differences in head movement were related to
TABLE II. Mean sentence recognition scores (percentage correct with SDs).
Acoustical environment
(SNR; RT) Age, (n ¼ 10 per age) Discussion condition Lecture condition
þ7 dB; 1.5 s 8 91.87 (5.34) 95.47 (1.59)
11 97.87 (1.28) 95.47 (5.32)
Adult 98.40 (0.76) 99.20 (0.73)
þ10 dB; 1.5 s 8 98.00 (1.63) 98.27 (2.14)
11 98.93 (0.89) 98.27 (1.30)
Adult 99.33 (0.82) 99.20 (0.56)
þ7 dB; 0.6 s 8 98.27 (0.90) 97.47 (1.96)
11 98.93 (1.21) 98.67 (1.05)
Adult 99.20 (0.56) 99.33 (1.16)
þ10 dB; 0.6 s 8 98.67 (2.62) 99.20 (0.39)
11 100.00 (0) 100.00 (0)
Adult 99.58 (0.76) 100.00 (0)
J. Acoust. Soc. Am., Vol. 131, No. 1, January 2012 Valente et al.: Acoustical conditions in a simulated classroom 241
the variance in comprehension scores in the discussion con-
dition. As in Experiment 1, the standard deviation (SD) of
the listeners’ head movements from the 0
centerline was
used to analyze looking behavior. A higher SD would indi-
cate a greater amount of head movement during the class-
room task.
Results of looking behavior were analyzed using a
three-way ANOVA, between-subjects design with age, RT,
and SNR as independent variables. The sample means are
displayed in Fig. 10. This analysis determined if looking
behavior was significantly different across age or acoustical
environment. While there was a general trend of decreasing
looking behavior as a function of age in all but the most
adverse environment, none of the main effects or interactions
reached significance (P > 0.05).
The impact of the amount of head movement during the
course of the experiment on a subject’s comprehension score
was determined. A scatter plot with looking behavior on the
abscissa and comprehension score on the ordinate is shown
in Fig. 11. While the amount of looking is negatively corre-
lated with comprehension score (1 additional degree of SD
in looking behavior yields a 0.2% drop in comprehension
score), this regression analysis was not significant,
(P > 0.05). For this set of subjects, comprehension score was
not significantly related to looking behavior. While increased
looking may suggest that listeners are expending more cog-
nitive resources processing the stimuli, the wide variability
across age group and tested environment suggest that more
research is necessary to determine the role of looking behav-
ior during a complex learning task.
Finally, the looking behavior tracks were analyzed to
determine the percentage of time during the course of the
experiment that the subject was looking at the active talker.
The sample means of this analysis are shown in Fig. 12. This
FIG. 8. Mean comprehension scores
on the classroom-learning task in the
discussion (left) and lecture (right)
conditions as a function of acoustical
environment for the three age groups
tested.
FIG. 9. Comprehension scores as a function of RT (left) and SNR (right)
for the discussion and lecture conditions.
FIG. 10. Average looking behavior during the classroom-learning task as a
function of acoustical environment for the three age groups (discussion con-
dition only).
242 J. Acoust. Soc. Am., Vol. 131, No. 1, January 2012 Valente et al.: Acoustical conditions in a simulated classroom
analysis revealed that, irrespective of looking behavior, sub-
jects visualized the active talker less than 50% of the time
across all ages and conditions. It appears that even in the age
group that exhibited the highest looking behavior (8-yr-old
children), the amount of time that looking behavior actually
resulted in visualizing the active talker was low. The
dynamic nature of the task, with fast moving dialogue
changes among talkers, similar to a realistic classroom dis-
cussion, revealed that subjects did not visualize a high per-
centage of the active talkers during the task. It is possible
that the poor relationship between looking behavior and
comprehension is related to the fact that even when listeners
tried to look at talkers in an effort to improve understanding,
they were unable to actually visualize the talker each time
he/she spoke.
Further research is necessary to determine if looking
behavior can be a significant predictor of comprehension
score across a larger set of acoustical environments if sub-
jects are able to correctly visualize the active talker at a
higher rate. In addition, tasks that allow for within-subject
comparisons across multiple acoustical environments may
provide additional information regarding the relationship
between looking behavior and comprehension in active lis-
tening tasks.
Results suggest that reducing the SNR from þ10 to þ7
has a more adverse effect than increasing the RT from 0.6 to
1.5 s. The addition of excessive noise on reduced task per-
formance compared with excessive reverberation is consist-
ent with results presented by others (e.g., Bradley, 1986;
Bradley et al., 2003; Yang and Hodgson, 2006).
In summary in Experiment 2, subjects were tested in
acoustical environments that elementary-aged children might
encounter in schools. Children and adults performed listen-
ing tasks similar to those found in classrooms to better
understand how children listen and learn. Despite differences
in performance across ages and acoustical environments,
sentence recognition was not affected by listening condition
(lecture versus discussion), and scores for all subjects were
high in all acoustical environments.
For the comprehension task, the younger elementary-
aged children performed more poorly than the older
elementary-aged children and adults in all conditions and
acoustical environments. This finding supports previous
research and suggests that younger students require a more
favorable acoustical environment for optimal academic
achievement (e.g., Crandell and Smaldino, 2000). Unlike the
sentence-recognition task, comprehension scores were more
adversely affected by listening in an active discussion com-
pared to a lecture in acoustical combinations of unfavorable
SNRs, RTs, or both. This finding suggests that the acoustical
conditions needed for understanding in a dynamic classroom
environment may vary depending on the task. It further sug-
gests that a comprehension task, rather than a sentence-
recognition task, is a more appropriate measure of children’s
performance in an active discussion. Including multiple talk-
ers and locations, as well as using a task-length similar to
classroom lessons, also may provide a more accurate repre-
sentation of performance in active discussions than tasks that
only require repetition of words and/or sentences. It was of
interest to examine the listeners’ looking behavior in the dis-
cussion condition to account for the differences in compre-
hension scores. Although a relationship was observed,
looking behavior was a significant predictor of comprehen-
sion scores only in the baseline condition.
IV. SUMMARY
The present study investigated how well school-age
children perform on speech- perception/intelligibility tasks
similar to the types of tasks they experience in classrooms.
These tasks included both a discussion (where a teacher and
multiple students presented information from multiple loca-
tions around the subject) and a lecture (where a teacher alone
presented material from a location in front of the subject).
By using a simulated classroom that included virtual room
modeling techniques as well as auditory-visual stimuli, the
experimental paradigm simulated a realistic environment to
a greater degree than previous studies that have presented
FIG. 11. Comprehension score as a function of looking behavior for all
subjects.
FIG. 12. Proportion of events visualized during the classroom-learning task
as a function of acoustical environment for the three age groups (discussion
condition only).
J. Acoust. Soc. Am., Vol. 131, No. 1, January 2012 Valente et al.: Acoustical conditions in a simulated classroom 243
test materials in sound booths, over headphones, or from a
single loudspeaker.
Not hearing the words a teacher is saying during the
course of class will make learning difficult. The implications
of excessive reverberation or a poor SNR are apparent; the
audibility of speech will be reduced; this has an additive
effect over the course of a typical-length classroom lesson or
discussion. Indeed, in the results presented in this experi-
ment, variance in performance in both children and adults
was seen across condition, RT, and SNR scenarios. This
effect was less apparent when subjects were merely required
to complete a sentence-recognition task, where subjects were
not required to listen, comprehend, and recall concepts after
a period time but rather repeat material quickly after hearing
it without long-term comprehension.
The lack of decrements in sentence recognition in
unfavorable environments may result in an underestimation
of the deleterious effects of poor classroom acoustics in daily
learning activities for students. The sensitivity to small
changes in SNR is apparent in the range of the comprehen-
sion scores in Experiment 2 for children and adults, which
were not seen to the same degree in the baseline classroom
tested in Experiment 1. Finally, the low POEV in all condi-
tions across both children and adults in both experiments
could indicate that subjects had a hard time keeping up with
the rapidly changing dialog of the learning task, but the
extent to which this would be expected to influence their
comprehension scores is still an open question. These results
highlight the need for diligent work during classroom design
to minimize the amount of masking background noise and
employ reverberation optimization techniques such those
described by Hodgson and Nosal (2002), Yang and Hodgson
(2006), and Bradley et al. (2003) for example. Results of the
decrement in performance reveal that depending on the com-
plexity of the learning task, even classrooms that meet ANSI
standards still may not be an optimal environment for effi-
cient listening and learning.
Continued research is needed to extend the age range
to include younger children and children with hearing loss
as well as to include tasks that involve multitasking (such
as listening to a lecture and taking notes) and divided
attention. By conducting studies that maintain a greater
degree of ecological validity, the impact of acoustical envi-
ronment on a child’s ability to learn and understand in the
classroom can be more fully understood. The impact of
this ecological validity can be investigated by another
potential topic for future study: determining the extent
including visual cues of the talkers has the ability to influ-
ence comprehension. This could be investigated by having
conditions that include auditory-only versus audio-visual
stimuli. Further analysis of looking behavior, such as the
ability to correctly identify talkers as they speak, may pro-
vide additional information about the effects of looking
behavior on comprehension.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the support for
this research through NIH grants R03 DC009675 (D. L.,
E. H., J. F.), T32 DC000013 (D. V.), T35 DC08757 (H. P.),
and P30 DC004662. The authors thank two anonymous
reviewers for helpful comments and suggestions on an early
version of this paper. Finally, the authors thank Roger Harp-
ster at the Boys Town National Research Hospital for assis-
tance with the setup of the laboratory space and videos.
APPENDIX
The following section describes the background noise,
clarity, and reverberation measurement for the current pa-
per. The background noise measurements and IRs were
measured and analyzed using the same methods described
in Sec. II A. Table III describes the octave-band back-
ground noise measurements for the simulated classroom
with no virtual acoustics added (labeled: “No VA”), and
with the þ10 and þ7 dB SNRs used for the experiment. In
addition, the background noise levels for the sound-treated
meeting room used for recording the discussion and lecture-
type stimuli are provided.
Table IV shows the octave-band RT (both EDT and T
30
)
and C
50
for the simulated classroom with no virtual acoustics
added and in the two RT-conditions used for the experiment
(0.6 s and 1.5 s). The internal T
60
for the ViMiC software’s
reverberator module is also provided when applicable. For
each of the measurements in the simulated classroom, the
values are the mean and SD of the five sound-source loca-
tions (see Fig. 2). Finally, data are provided for the meeting
room used to record the discussion and lecture-type stimuli,
both using a far-field source-receiver distance (3.5 m source-
receiver distance) and a close-microphone source- receiver
distance as was used in recording the individual stimuli
(10 cm).
TABLE III. Octave-band filtered background noise measurements for the acoustical environments used for the
study.
Octave-band center (Hz)
Measurement/location 125 250 500 1000 2000 4000 8000
Meeting Room 45.0 38.2 33.9 25.4 19.9 19.5 20.2
Simulated classroom (No VA) 52.9 40.3 29.0 22.8 20.5 19.6 20.4
Simulated classroom þ 10 SNR 58.0 55.1 45.7 43.9 38.4 34.4 25.3
Simulated classroom þ 7 SNR 59.9 58.1 49.4 47.2 41.9 37.6 28.0
244 J. Acoust. Soc. Am., Vol. 131, No. 1, January 2012 Valente et al.: Acoustical conditions in a simulated classroom
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TABLE IV. Octave-band filtered C
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mean and SD for each of the five sources is given. The meeting room close-microphone measurement was made using a 10 cm source-receiver distance similar
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EDT (s) 0.40 (0.10) 0.35 (0.04) 0.25 (0.08) 0.25 (0.03) 0.23 (0.04) 0.24 (0.02) 0.22 (0.01)
T
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Simulated classroom, 0.6 RT C
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EDT (s) 0.58 (0.14) 0.59 (0.08) 0.62 (0.05) 0.6 (0.02) 0.6 (0.04) 0.5 (0.05) 0.37 (0.08)
T
30
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Simulated classroom, 1.5 RT C
50
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EDT (s) 0.96 (0.12) 1.10 (0.12) 1.44 (0.04) 1.43 (0.11) 1.17 (0.01) 0.84 (0.04) 0.47 (0.02)
T
30
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Meeting room (3.5 m) C
50
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EDT (s) 0.60 0.73 0.74 0.68 0.81 0.75 0.56
T
30
(s) 1.12 1.12 0.93 0.99 1.13 1.21 0.98
Meeting room (10 cm) C
50
(dB) N/A 17.60 13.40 10.70 13.00 13.40 16.90 19.90
EDT (s) 0.05 0.05 0.03 0.17 0.05 0.01 0.01
T
30
(s) 1.11 1.06 0.83 0.86 0.99 0.98 0.71
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