Average Number of Syllables per Second

Average Number of Syllables per Second

6.60

6.40 4

7.00

6.20
6.00
5.80
5.60 4
5.40 4
§.20
5.00 4
2 3

a

) ey) (16)

Workdoad
(Number of Utterances)

Figure 5. Average Speaking Rate (Syilables/Second) as a Function of Workload for Pasticipant 1 in the
East Heavy Scenario.

4 5 8 7 8 9 10 "1 12
@ @ {28) 26) a] 8) (2)

6.00 q
5.00 4

4.00 4

3.00 4

200

1.00 4

0.00 4

-

2 3 4 5 6 7 8 9 10 oh 12 13
eo mm & om BOB BOB OF GD 1 CB Bn &%
Workload
{Number of Utterances)

Figure 6. Average Speaking Rate (Syllables/Second) as a Function of Workload for
Participant 1 in the West Heavy Scenario.

A3

4 15
® tO)



Table 1. Narrow Focus Analysis Exploring the Relationship between Acoustic Measures
and Workload for Participant 1 (East Specialty)

Dependent Measure East Scenario West Scenario
Speaking rate r= 0.038 r= 0.005
(p=0.64) (p=0.20)
Pause frequency r= -0.058 r=-0.170
(p=0.48) (p=0.02)
Pause duration r=-0.076 r=-0.059
(p= 0.36) (p= 0.42)
Velar VOT r=-0.581 r=0.112
(p=0.70) (p= 0.47)
Alveolar VOT r=-0.106 r=0.113
(p=0.49) (p=0.47)
Pitch period r=-0.148 r=-0.273
(p=0.33) (p= 0.07)

Table 2. Speech Measures Presented by Light and Heavy Workload

Dependent Measure Light Scenario Heavy Scenario
Speaking Rate (syV/per sec) 6.17 5.83
Standard Deviation 1.27 1.25

Pause Frequency (pauses per word) 008 006
Standard Deviation 009 008

Pause Duration (in ms) 10.47 7.39
Standard Deviation 17.93 14.59

The results of correlational anzlyses performed for
each acoustic measure and workload are summarized
in Table 1. The standard scientific convention of
setting p < .05 was used to indicate statistically

significant results. Although pause frequeacy and
workload for the West Heavy scenario were signifi-
cantly correlated, the practical significance of this
result is limited. Only 3% of the variability in pause
frequency was accounted for by workload. As work-
load increased, pause frequency decreased. Figures 5-
16 (Appendix A) show average speaking rate, average
pause frequency, average pause duration, velar VOT,
alveolar VOT, and pitch period as a function of
workload. An examination of the FO tracks revealed
variations in contour dynamics within the range

noted in previous studies of single speakers (Atkinson.
1973; Lieberman et al, 1984) and were therefore
concluded to be no: icant.

3.2 Broad Focus, Participants 2 - 12

Presented in Table 2 are summary statistics for
each of the speech measures and workload. Partici-
pants 2-12 dataon each of these measuresare reported
in Figures 17-19 and can be found in Appendix A.

3.2.1 Speaking Rate. Figure 17 shows the average
speaking rate (SR) for each participant during the
simulation of the Light and Heavy traffic scenarios.
Avisual inspection of the data reveals that overall, there
appears to be a trend towards faster speech (more

syllables per second) during the Light, compared with


VOT (me)

VOT (me)

30.00

25.00 4

20.00

18.00

10.00 4

4

3 a 8 6 7 8 9 10 abl 12
io) t) o » 1% cu) =.) o o

4] a Om 1
‘Workdoed
(Humber of Uterences)
Figure 13. Average VOT of [i] as a Function of Worldoad for Participant 1 in the East Heavy Scenario.

0.00 4

: 3 4 5 6 7 8 9 10 ww 12 13 1 15
6 ° Ss * iC) id @ i) © LU) iO) Av)

4

Workload
(Namber of Utterances)

Figure 14. Average VOT of ft] as a Function of Workload for Participant 1 in the West Heavy Scenario.

A-7


Section

1.0

2.0

4.0

5.0

6.0

TABLE OF CONTENTS

INTRODUCTION ..ccccccccsssssssssssssesssssceseseesssssensssnnees ea soos seseccseeeeeeeses ns |

1.2 Measures of Interest ...

METHODS .
2.1. Participants ..
2.2 Equipment ..

2.3
2.4
25
2.6
2.7 Speech Analysis Procedures.

RESULTS..... co
3.1 Narrow Focus, Participant } aren
3.2 Broad Focus, Participants 2-12 .......-..+00 esnsvsccenusccsanvecssenenscensesscsusseceuneceonsvees. ee)

DISCUSSION .......s.scsees eos a - a evsconeenvee ssseaeeanes LO

REFERENCES. .....-sscsssssosessnesees sussaneconsenscsnecoeeenseeases oossesenees ee 12

APPENDIX A ...



Average Number of Pauses per Second

0104

0.08 4

0.06 4

0.04 4

0.02 4

0.00 4

Average Number of Pauses per Second

tli

i)

o @ ® a) (0) @e) (za) (18) co) @
Workload
(Number of Utterances)
Figure 7. Average Pause Frequency (Number of Pauses/Number of Words) as a Function of Workdoad for
Participant 1 in the East Heavy Scenario.

0.35

0.20 4

0.15 4

0.10

+ 2 a 4 5 8 7 8 ry 10 014 424848:
o ® OO ON 8) ° @
Workloed
(Number of Utterances)

Figure 8. Average Pause Frequency (Number of Pauses/Number of Words) as a Function of Workload for
Participant 1 in the West Heavy Scenario.

A-4

18
a



(Pause Duration / Word (in ma)

30.00

25.00

20.00

10.00

5.00

0.00

fm Lignt
|B Heavy

Figure 19. Average Pause Duration for Each Participant in the Light and the Heavy Conditions.

A-10

‘SXUS. GOVERNMENT PRINTING OFFICE: 1998 -coo-ste eens,


60.00

50.00

40.00 4

30.00

Vor (ene)

20.00

10.00 |

° 10 " 12

0.00
” ® tO) ow
Workiosd
(Number of Utterances)
Figure 11. Average VOT of [k] as a Function of Workdoad for Participant 1 in the East Heavy Scenatio.
60.00
50.00 |
40.00 a
? 30.00
20.00 ]
10.00
oa J
3 4 6 6 7 8 9 10 1t 12 13 4 \ 15
® eo ® © a ic) @ A) © @ ® m o

Wordoed
(Number of Utterances)
Figure 12. Average VOT of [k] as a Function of Workdoad for Participant 1 in the West Heavy Scenario.

A-6


Lieberman, P., and Blumstein, S.E. (1988). Speech
physiology, speech perception, and acoustic phonet-
ics. Cambridge: Cambridge University Press.

Lieberman, P., and Michaels, $.B. (1962). Some as-
pects of fundamental frequency, envelope ampli-
tude and the emotional content of speech. Journal
of the Acoustical Society of America, 34, 922-927.

Lieberman, P., Protopapas, A. and Kanki, B.G. (1995).
Speech production and comprehension deficits
on Mt. Everest, Aviation, Space, and Environmen-
tal Medicine, 66, 857-869.

Lieberman, P., Kako, E.T., Friedman, J., Tajchman,
G., Felldman, L.S., and Jiminez, E.B. (1992).
Speech production, syntax comprehension, and
cognitive deficits in Parkinson’s disease. Brain
and Language, 43, 169-189.

Lieberman, P., Karz, W., J , A., Zimmerman,
R, and Miller, M. (1984). Measures of the sen-
tence intonation of read and spontaneous speech
in American English. Journal of the Acoustical
society of America, 77, 649-657.

Lisker, L. and Abramson, A.S. (1964). A cross language
study of voicing in initial stops: Acoustical mea-
surements. Word, 20, 384-342.

13

Muller, J. (1848). The physiology of the senses, voice and
Baly, Trans.). London: Walton and Maberly.

Prinzo, O.V. (1996). An analysis of approach control!
pilot voice communications, Federal Aviation Ad-
ministration, Office of Aviation Medicine Tech-
nical Report DOT/FAA/AM-96/26, Washing-
ton, DC. Available from: National Technical
Information Service, Springfield, VA 22161; or-
dering no. ADA274457.

Prinzo, O.V. & Britton, T.W. (1993). AT C/pilot voice
communications: A survey of the literature. Federal
Aviation Administration, Office of Aviation Medi-
cine Technical Report DOT/FAA/AM-93/20,
Washington, DC. Available from: National Tech-
nical Information Service, Springfield, VA 22161;
ordering no. ADA317528.

Sataloff, R.T. (1992). The human Voice. Scientific
American, 267, \08-115.

Waters, J., Nunn, S$. Gillcrist, B. and VonColln, E.
(1995). The effect of stress on the glottal pulse.
Proceedings, ESCA - NATO Tutorial and Research
Workshop on Speech under Stress, pp. 9-11. Portu-
gal: Colibri, Sociedade de Artes Graficas,


standard 101-style keyboard and computer mouse.
The TRACON workstation was housed in a room
separate from the ghost pilot workstation. The work-
stations communicated to each other though a
LANtastic network operating system.

2.2.2 Video Recording Equipment. A Sony
Handycam CCD-TR81 video Hi8 camcorder,
mounted on 2 Bogen 3165 Tripod, was positioned
approximately 4 meters to the left and 6 meters in
front the controller's workstation. Only the radar
display, back of the controller, and hand movements
were recorded. The audio/video output of the Sony
Handycam went to 2 3-set Audio/Video Distribution
Amplifier (15-1103), displayed on a Sony Color
Video Monitor PVM2530 equipped with 2 Sony SS-
X6A speakers, and recorded by a Sony Video Cassette
Recorder SVO-1610 on standard VHS T120 Cassettes.

2.2.3 Audio recording equipment. A Sony Electret
Condenser {ECM-77B) was attached to a
Shurlite headset and positioned i 15cm
from the controller's lips. The output signals of the
microphone were amplified by a Panasonic Audio Mixer
WR-450 and then sent to a Sony Digital Audio Re-
corder PCM-2700, where they were time stamped and
stored on 120-minute BASF DAT Cassettes.

2.3. Technical Support Staff

A certified human ghost pilot from the FAA Acad-
emy was trained on the 6 scenarios and served as the
ghost pilot in this study. A recently retired FPL
controller served as the subject matter expert. He
constructed the scenarios, trained the ghost pilot,
developed briefing materials, and provided the ghost
pilot with on-line instructions while the controller
worked the scenario. Several staff members from the
TRACON facility provided expert information and
guidance in the development of the airspace, proce-
dures, and traffic. Also, several controllers worked
the scenarios at the workstation prior to the start of
the experiment, reviewed each scenario, and pro-
vided guidance to ensure fidelity and realism.

2.4 Materials

2.4.1 Scenario Construction. The number of air-_
craft requiring radar service was experimentally ma-
nipulated to simulate high and low workload scenarios.
For example, light traffic density involved approxi-
mately 1 aircraft communicating with the ATCS per
minute and heavy traffic involved 2 aircraft commu-
nicating with the ATCS per minute. Light traffic
scenarios were developed from heavy traffic scenarios
by simply removing 50% of the aircraft from the
scenario. The Feeder’ East, Feeder West, and Arrival®
positions were simulated. For example, the East spe-
cialist will never work on the West side. Traffic
density was crossed with simulated positions to pro-
duce 6 scenarios.

2.4.2 Ghost Pilot Communication Scripts. Based
on analyses performed by Prinzo (1996) on ATC/
pilot voice communications acquired from the par-
ticipating TRACON facilities, normal and problem-
atic pilot communication scripts were constructed
and fully counter-balanced for use in each scenario.
The scripts were used by the ghost pilot, who initi-
ated calls to ATC at pre-determined times and re-
sponded to messages generated by the controller.

2.4.3 Computer-Generated Pilot Responses. Each
Non-Target aircraft response was generated by the
TRACONpro software. Aircraft call signs, ICAO
alphabet, and phrases used in ional communica-
tions were recorded, edited, and stored as . WAV files...
Theintelligibility and realism of the computet-generated
responses was evaluated by the FBI speech-processing
laboratory at Quantico, VA. A computer-generated
response was selected at random and compared with the
live recording of that message by the ghost pilot. A-visual
inspection of the spectrograms revealed that the visual
characteristics of the sound waves were the same and
produced by the same person.

2.5 Procedure

Upon arrival to the TRACON simulation labora-
tory on Day 1, the controller was briefed on the
purpose of the study, instructed on Verbex voice

7A Feeder sector is transition area in the tetminal airspace. The feedet controller is responsible for providing separation and sequencing
inbound aircraft toward che final approach course. The feeder controller will hand off to che arvival controller, TM

* The Arrival sector is located within the terminal airspace. The controller provides separation and sequencing of aircraft on the
approach. The arrival controller will hand off to the tower controller.


tensions of various laryngeal muscles and the alveolar
air pressure. The fundamental frequency of phona-
tion (FO) is, by definition, the rate at which the vocal
folds open and close. The perceptual response of
human listeners of FO is the perceived pitch of a
speaker’s voice. Young children, for example, have
high FOs during phonation (over 300 Hz); their
voices, thus, are “high pitched.” The average FO for
men is 125 Hz and over 200 Hz for women. Acoustic
energy occurs during phonation at the FO and at the
harmonics of the FO. For example, if FO is 100 Hz,
energy can occur at 200 Hz, 300 Hz, and so on. The
amplitude of the harmonics typically decreases as
frequency increases for the phonatory patterns typi-
cal of human speech. During the course of speech
production, speakers constantly modify the funda-
mental frequency of phonation at linguistic ends
(ie., the speaker's voice will drop at the end of the
utterance to signal the listener that the message is
coraplete). Distinctions in dialect, as well as semantic
distinctions, can be transmitted by deliberate modi-
fications of the fundamental frequency contour of an
utterance. In English, for example, yes-no questions
are usually signaled by a rise in FO at the end of a
sentence and stressed words by local peaks in FO
(Lieberman, 1967). For example, “Are you sure?”
signals that a question is being asked because of the
tise in the FO when the word “sure” is produced. The
response “Yes, I’m sure” has a lowered FO for the same
word occurring at the end of the sentence.

Noise sources tend to have acoustic energy evenly
distributed across all frequencies. Noise sources can
be generated at constrictions along the airway leading
out from the trachea when the airflow becomes tur-
bulent. Noise can be generated at the larynx by
forcing air through the partly abducted vocal cords
as, for example, at the start of the word “hat.” Noise
can also be generated by forcing air through constric-
tions in the SVT. For example, the constriction
formed in the mouth when the tongue is raised close
to the hard palate in the initial consonant of the word
“shoe” generates the noise source of the initial conso-
nant. Momentary bursts of noise excitation typically
occur on the release of stop consonants such as [p]
when the lips open, at the start of the word “pig.” The
burst is momentary because the turbulent noise
abruptly ceases as the airflow changes from turbulent
to laminar flow as the lips open wide.

The time interval between the burst of a stop
consonant and the onset of phonation of the fllow-
ing vowel is the voice onset time (VOT). VOT
differentiates English “voiced stop” consonants like
[b), [d], and [g] from their unvoiced counterparts [p],
[dl], and [k], respectively. In order to produce a [b], a
speaker must initiate phonation soon after opening
the lips (within about 20 ms.) to release the pressure
in the vocal tract. In contrast, phonation is delayed
for 40 ms. or more after lip opening in a [p]}. Similar
timing distinctions differentiate [d]s from [t]s and
[g]s from (k]s. Figure 1 shows the waveforms for a [b]
and a [p] produced by the same speaker, where the lip
opening (identified by a visible burst) and the onset
of phonation (evidenced by periodicity in the wave-
form) have been marked. The time delay between the
marks is the VOT. Normally, speakers of English and
many other languages maintain the distinctions be-
tween voiced and unvoiced stop consonants by keep-
ing the VOT regions of the two separated by at least
20 ms.

1.2 Measures of Interest
~ Four primary measures of interest were selected as
dependent variables: (1) speaking rate, (2) hesitation,
(3) fundamental frequency (FO), and (4) voice onset
time (VOT). Speaking rate (syllables/second) might
covary with workload in either of two directions. It is
possible that, because an increase in work load re-
quires an increase in the number of communications
in a fixed amount of time, speaking rate would be
increased to “squeeze in” more information ina given
time period. Conversely, it might be the case that, as
workload increases, speaking rate decreases. It has
been shown that verbal “hesitation,” which is typified
by brief silence, increases with task difficulty and
with the quality of a cognitive solution to a given task
(Eisler 1968). It has further been shown that there is
an inverse relation between the amount of hesitation
and speaking rate (Eisler 1968). Assuming increasing
workload is equivalent to increasing task difficulty,
speaking rate may decrease as workload increases.
In light of Eisler’s findings, hesitation was deter-
mined as a measure of potential interest. Eisler estab-
lished that in general, 40%-50% of speech is actually
silence; that is, speech is not the continuous flow of
sound indicated by our perception. Three types of
silence can be found in connected speech: 1) the


AN Acoustic Anatysis or ATC CoMMUNICATION

“The true use of speech is not so much to express our wants as to conceal them.”

1.0 INTRODUCTION

Radio communication isthe primary means
. pilots and air traffic control specialists (ATCSs)' trans-
mit verbal messages between each other. Controllers
learn to speak a particular grammar using a pre-defined
cadence during their initial training at the FAA Acad-
emy and at their assigne? air traffic control facility. In
addition co the verbal message transmitted orally, the
experience, controllers learn to conceal! potential emo-
tional content from their speech. While researchers have
not yet identified consistently reliable quantifiable fac-
tors, several aspects of speech production have been
shown to be related to physiological and task-induced
stress (e.g., Lieberman et al., 1995; Absil et al., 1995;
Benson 1995; Waters et al. 1995; Cummings and
- Clements, 1990; Frick 1985; Licberman and Michaels,
1962; Coster 1986; Kagan et al., 1988). The investiga
tion of acoustic correlates of perceived emotional stress
and cognitive load is an active field of inquiry in aviation
as well (sce Prinzo and Britton, 1993 for a review).
The objective of this scudy was to identify acoustic
properties of air traffic control communications asso-
ciated with changes in workload. Within the context
of this study, workload was determined by counting
the sumber of aircraft for which a controller was
actively providing radar service when 4 message was
produced. The fewer number of aircraft receiving
radar services, the lighter the workload. While ATCSs
worked simulated traffic under heavy and light den-
sity, their speech was captured onto Digital Audio
Tape (DAT) and later analyzed to establish whether
previously identified acoustic factors show a reliable
relationship to simulated workload.

— Oliver Goldsmith (1728-1774)
The Use of Language

1.1 Background ~
Human speech production results from the activ-

ity of three functionally distinct systems; (1) the
subglottal lungs, (2) the larynx, and (3) the
supralaryngeal airway—the supralaryngeal “vocal
tract” (SVT). The acoustic consequences of the physi-
ology of these systems have been studied since the
early 19% century when Muller (1848) formulated
what has come to be known as the “source-filter”
theory of speech production. Muller noted that the
outward flow of air from the lungs usually provides
the power for speech production. If the human audi-
tory system were capable of perceiving acoustic en-
ergy at extremely low frequencies, we would “hear”
the expiratory airflow. However, the acoustic energy
present in the outward flow of air from the lungs is
inaudible. The “sources” of acoustic energy for speech
are generated by modulating the outward, expiratory
flow of air.

Two fundamentally different sources of noise that
provide the acoustic energy for the production of
human speech are periodic phonation and turbulent
noise (Borden and Harris, 1984; Ladefoged, 1962).
Periodic phonation is the result of the activity of the
larynx. The vocal folds of the larynx, which are
extremely complex structures, move inwards and
outwards, converting the steady flow of air flowing
outwards from the lungs into a series of “puffs” of air.
This process repeats itself many times, creating a
train of impulses. The number of times the vocal
folds open and close per second (i.e., cycles of repeti-
tion) directly determines the lowest frequency of the
sound that is produced (Sataloff, 1992). Both the
basic rare and the detailed airflow through the pho-
nating larynx can be modulated by adjusting the

_ "For ease of reading, the term “controlles” will be used synonymously with air traffic control specialist.


APPENDIX A

FIGURES 5-19

A-l



It reflects a participant's ability to precisely sequence
the maneuvers of the tongue, lips, velum and larynx
that are necessary to produce human speech. Studies
of Broca’s aphasia (Blumstein et al., 1980; Baum et
al., 1990), Parkinson’s Disease (Lieberman et al.,
1992) and mountain climbers breathing low oxygen-
content air in the course of an ascent of Mount
Everest (Lieberman et al., 1995) show that control of
VOT deteriorates. In these cases, abnormal VOT
production is correlated with decrements in reason-
ing and sentence comprehension. As such, ithas been
suggested that VOT production may be used as an
index of cognitive functioning. Accordingly, we
measured the VOT of the word-initial voiceless velar
stop [k] and the word-medial voiceless alveolar stop
[t] from the word ‘contact’ when it occurred utter-
ance-finally in the phrase ‘contact approach’ (26% of

Figure 2, TRACONpro simulator.

utterances). This data set was chosen to minimize
variation attributed to concext, both lexical and

phrasal.
2.0 METHODS

2.1 Participants

Twelve full performance level (FPL) air traffic
control specialists from a level 5 Terminal Radar
Approach Control‘ (TRACON) facility completed
this study. There were 5 East specialty and 7 West
specialty air traffic controller specialists (9 male and
3 female) who, collectively, had 13.17 mean years of
terminal experience (SD = 3.49) with 9.88 mean
years (SD = 3.19) at the full performance level. The
East specialist only works sector positions that pro-
vide radar services to aircraft arriving from the east
and the west specialist only works sector positions
that provide air traffic services to air-
craft arriving from the west.

2.2 Equipment

2.2.1 TRACON and Ghost Pilot
Workstations. Wesson Internat-
ional’s TRACONpro® software was
installed on two 486/66 MHz DX2
personal computers. Each worksta-
tion displayed radar traffic on a 21”
multi-scanning capable monitor with
high-sesolution video adapters (1280
x1024x256). As shown in Figure 2,
the TRACON workstation included
an amber 14” monitor for displaying
automatic terminal information ser-
vice® (ATIS), a track ball, and auto-
mated radar terminal system® (ARTS
IMA) simulated keyboard, standard
101-style keyboard, Verbex 6000
Voice Systems continuous voice rec-
ognition “slave” computer board, push-to-talk head-
set, and Soundblaster 16-bit digitized pilot response
sound board. The ghost pilot workstation included a

4 Aterminal radar approach control (TRACON) facility is associated with an air traffic control tower thar uses radar to provideapproach

control services to aircraft.

5 Automatic Terminal Information Service provides pilots with continuous broadcast of recorded nonradar information in selected

terminal areas. information includes time, w:
on a secondary monitor next to the radar display.

eather, runway, and other essential but routine information. This information is displayed

©The Radar Tracking and Beacon Tracking Level of the modular, programmable automated radar terminal system. ARTSIIIA detects,
tracks, and predicts primary as well as secondary radar-derived aircraft targets. This more sophisticated computer-driven system
upgrades the existing ARTS ILI system by providing improved tracking, continuous data recording, and fail-safe capabilities.


Pitch Pertod (ra)

Pitch Period (ms),

9.60 4

9.404

9.20 4

9.00

8.80 4

8.40

8.20 4

8.00.

|

4 5
@ 1 @

6. 7 0
i) @ (13) m @ .

.b 12,
i)

r)
10)
‘Workload
{Number of Utterances)
Figure 15. Average Pitch Period from Phrase Final Syllable “proach” as a Function of Workload for
Participant 1 ‘in the East Heavy Scenario.

9.80
9.604"
9.40
920 . :
9.004
8.80 : : eae
8.60 * .
a0 : : | ‘
8.20 : ‘ ey
8.00 : : :
7:80 4 ca :

3 4 6 6 7 8 9 10 4 12 13. 44 15

“ 2) ® © 0 Oo. @ r) © 1) o

: Workload
(Number of Utterances)
Figure 16. Average Pitch Period from Phrase Final Syllable “proach” as a Function of Workload for
Participant 1 in the West Heavy Scenario.

A-8



DOT/FAA/AM-98/20

Office of Aviation Medicine
Washington, D.C. 20591

An Acoustic Analysis of
ATC Communication —

O. Veronika Prinzo

Civil Aeromedical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

Philip Lieberman

Emily Pickett Nv

Brown University

Providence, RI 02912 >
<>
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co

July 1998 om ]

: be Od
no
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Final Report = J
<>
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|

This document is available to the public _
through the National Technical Information
Service, Springfield, Virginia 22161.

@ ;

U.S. Department Pe :
of Transportation oo

Federal Aviation ss ~
Administration


eu Se

PETS

expen oren | Lom | SKIT

Figure 3. BLISS system display showing the waveform of the word
“pig.” Cursors L1 and R1 mark VOT of the initial consonant [p].

listen to the waveform delineated by any set of cur-
sors; the sectioned waveform can be transferred to
another file, “spliced” to any other file, reduplicated,
scaled up or down in amplitude, inverted, etc. Figure
3 illustrates some of the features of the BLISS system.
The amplitude of the speech signal at the onset of the
word is displayed on the ordinate as a function of
time which is plotted with respect to the abscissa.
‘The upper part of Figure 3 shows various aspects of
the BLISS system’s “headez.” It identifies the name
the stored audio file, the cursor positions, and the
waveform of the complete file, i.e., the word “pig.”
The lower boxes control a number of parameters of
the BLISS system by means of 2 mouse and display
the chosen parameter values. “Points” indicate the
number of points that are displayed on the screen;
they can be varied from 32 to 2480, allowing the
operator to view and manipulate the signal with
different temporal resolution. The “Delta” command
instructs the system to display every Xth data point,
compressing the signal. “Wave cont” is a switch that
can be set to display individual data points, or as in
the display of Figure 3, interpolare between data
points. The “Bank” box allows 1 of 4 sets of cursors
to be displayed and moved. “Expand” transfers the
severse-field (black background) display from the

upper waveform display to the full screen. “Listen”
allows the operator to listen to the section between
any set of cursors on the total waveform displayed
above. The “open” and “load” boxes are used to open
new files and to transfer data to these files, e.g., the
waveform between any set of displayed cursors.
Forall participants, speaking rate, pause frequency,
and pause duration were generated from the utter-
ances selected as described above. Speaking rate
(syllables/second) was computed from the number of
syllables per utterance and utterance duration in ms.
Because elisions and contractions (e.g., “merican” for
“American”) were common, only syllables actually
uttered, as determined by listening to the speech
sample and by visual examination of the waveform,
were counted, rather than number of syllables pre-
scribed by standard English pronunciation. Utter-
ance duration was measured by placing cursors at the
onset and offset of speech as determined by visual
examination of the waveform and by listening to the
speech sample. Pause frequency (number of pauses/
number of words) was computed from the number of
Pauses per utterance and the number of words per
utterance. The pauses in speech are normally of too
short a duration to be auditorily perceptible. Thus,
pauses were identified by visual examination of the


training procedures, completed voice-training on a
limited vocabulary, and gained familiarity and expe-
rience with the voice recognition system by working
a 15 minute practice scenario on a generic airspace.
Then full voice training commenced. Since it took
several hours to complete voice training, the control-
ler took several breaks while training the Verbex
system on his/her voice characteristics. When com-
pleted, the controller was given a 15 minute facility-
specific scenario to work during which the SME
determined whether additional voice training was
warranted and provided the controller with addi-
tional practice on the simulator.

Prior to beginning the experiment on Day 2, the
controller once again worked a generic practice sce-
nario. The first experimental simulation was loaded,
the audio/video equipment turned on, and the con-
troller received a standard position relief briefing
from the SME. The controller used standard phrase-
ology and followed facility proceduies to provide air
traffic services for aircraft during the 35-45 minute
scenario. Afterwards, the controller took a break
while a new scenario was loaded. This procedure was
repeated until the 6 experimental scenarios were
completed. A 45-minute break. for lunch was pro-
vided. The following constraints were imposed on
the order of scenario presentation: (1) The controller
did not work 3 consecutive high traffic scenarios, (2)
the controller worked traffic on each of the 3 posi-
tions before working traffic on the same position
again, and (3) all controllers worked the Arrival
position first.

2.6 Derived Measure of Workload

All transmissions were transcribed verbatim by a
retired air traffic control specialist. An aircraft was
counted as being under positive control once it estab-
lished initial contact with the controller. It was no
longer under positive control after the controller
completed the 2-stage hand-off procedure: 1) an
automated radar hand-off and 2) transfer of radio
communication to the next controller in the se-
quence. The number of aircraft on frequency at the
time the controller made a transmission was recorded
next to that transmission.

2.7 Speech Analysis Procedures

Two approaches were taken in the speech analysis,
narrow and broad. First, a detailed examination of
the speech of a single controller (Participant 1) was
performed to look for reliable relationships within a
scenario. Measures were taken from all utterances
produced by this participant in the Feeder East Heavy
and Feeder West Heavy scenarios. Heavy traffic sce-
narios were selected because they contained the most
aircraft for a controller to provide radar services.
They should reflect a light workload at the onset of
the simulation and build to a heavy workload-as the
simulation progressed. Workload was determined
from the total number of aircraft on frequency when
the controller began speaking. East Heavy workload
varied from 1-12 aircraft on frequency and West
Heavy workload varied from 1-15 aircraft on fre-
quency. Also, the increase in the number of aircraft
increased the total number of transmissions available
for analysis. ,

Second, a subset of utterances produced by the
remaining participants was analyzed to assess the
generalizability of Participant 1 results. Utterances
were selected to achieve maximal workload contrast
for each participant. For each participant, 10 utter-
ances were identified and digitized. Those utterances
corresponded to the 5 lowest workload utterances
from the Light version of the scenario, simulating
that participant’s specialty, and the 5 highest work-
load utterances from the Heavy version of the sce-
nario, simulating the participant’s non-specialty
sector.’ All speech signals were sampled at 16 bits _
quanitization at 20,000 samples per second; the digi-
tized signal was stored in audio files.

The analysis was performed using the interactive
BLISS speech analysis system developed by John
Mertus (Lieberman and Blumstein, 1988). The BLISS
system permits trained operators to monitor and
modify analysis parameters at virtually all: stages of
analysis, thereby minimizing artifacts that otherwise
can be introduced by most commercially available
speech analysis software. The BLISS system allows.
operators to view the waveform and position 4 inde-
pendent sets of “cursors,” ¢.g., left cursor LO and
right cursor RO, on the waveform. The operator can

° For example, for an East specialty controller, the 5 lowest workload utterances from the East Light scenario were contrasted to the

5 highest workload utterances from the West Heavy scenario.


yee

[Pp]

Figure 1. Speech waveform segments corresponding to a [b] and a [p] spoken by the
same speaker under identical conditions. Cursors have been placed at the onset of
the burst that was caused by opening the lips (L1) and at the onset of periodicity that
indicates vocal fold vibration (R1). The marked interval, Voice Onset Timing (VOT) is
used by speakers and listeners to differentiate the two types of consonants in word-

initial positions.

discontinuity of phonation that occurs in articula-
tory shifts, ¢.g., when two stop consonants follow
each other; 2) discontinuity of phonation attribut-
able to hesitation; and 3) the gap in speech required
for inhalation. The second type of silence, hesitation
pauses, is associated with complexity of general plan-
ning, task difficulty and the quality of a cognitive
solution. Eisler and her colleagues showed that a
person making reasoned responses to a question had
longer type 2 pauses than a person responding in an
automatic, non-engaged manner. Pause duration thus
can reflect “thinking time.” Therefore, increased
hesitation may be due to an increase. in pause fre-
quency, pause duration, or both. Accordingly, we
used 2 measures to examine hesitation; number of
pauses per word and average duration of pauses per
word. Again, if an increase in workload results in an

increase in cognitive load or thinking time, hesitation,
as reflected in either measure, may increase.

Although it is generally accepted that fundamental
frequency (FO) is affected by physiological and psy-
chological stress, there is conflicting evidence as to
which specific properties of FO are involved (e.g.,
Lieberman et al., 1995; Absil et al., 1995; Benson
1995; Waters et al., 1995; Cummings and Clements,
1990; Frick 1985; Lieberman 1963).. We selected a
global and a local measure: the overall pitch contour
of an utterance, and the pitch period? of the highest
amplitude portion of the second vowel in the word
“approach” when it occurred in the final segment of
an utterance, that is the utterance-finally.?

VOT, measured as the time interval between the
burst stop consonant and the onset of phonation, is
an objective acoustic measure of speech production.

2 The pitch period is also commonly referred to as fundamental frequency determination.
3 Often, the judgment of pitch for aperiodic sounds is influenced by the frequency measured by hertz, the number of cycles per second

(Hz), at which the amplitude is highest.


Average Pause Duration per Word (ms)

Average Pause Duration per Word (ms)

25.00
20.00 :
15.00
10.00
| ] ;
0.00 4 | | i
2 3 4 6 6 7 8 9 10 "
a) @ o @ (28) i) (28) 2) (16) @)
Workload
(Number of Utierances)
Figure 9. Average Pause Duration (Duration of Pauses/Number of Words) as a Function of Workload
for Participant 1 in the East Heavy Scenario.
40.00
95.00 4
30.00 4
25.00
20.00 4
18.00 4
10.00
5.00
0.00
1 2 3 4 5 6 7 8 9 10 011 1200 1300 44.48
oy) Oo Oo OF 1) em 6M UH OD aw o
Workload
(Number of Utterances)

Figure 10. Average Pause Duration (Duration of Pauses/Number of Words) as a Function of Workload for
Participant 1 in the West Heavy Scenario.

A-5



ACKNOWLEDGMENTS

This research was sponsored by the Federal Aviation Administration's Office of
Aviation Safety Analysis (ASY-200), and the Evaluations and Investigations Staff of Air
Traffic Services (AAT-20).

iti


Table 3. Broad Focus Analysis Exploring the Relationship between Mean Normalized

Speech Measures and Workload
Dependent Measure Light Scenario Heavy Scenario

Mean Normalized Speaking Rate (SR) 1.03 0.97
Standard Deviation (0.19) (0.18)

Mean Normalized Pause Frequency (PF) 1.41 0.59
Standard Deviation { 1.90) (0.96)

Mean Normalized Pause Duration (PD) 1.46 0.54
Standard Deviation ( 2.04) (1.20)

the Heavy traffic simulation. Sixty-four percent (7
out of 11) of the participants show this pattern, 3
show the reverse pattern, and 1 participant’s SR dczs
not change across simulations.

To detecmine whether the observable trend to-
wards faster speech in the Light simulation was sig-
nificant on a gror:p basis, a normalized SR mezsure
was generated by computing, for each particinznt and
each SR value, the ratio of chatvalue to the participant's
mean SR. The resulting ratio values were combined
into group measures and are presented in Table 3. A
statistical comparison then was performed on the
normalized SRs from the Light and Heavy simula-
tions. The mean normalized SRs for the Light and
Heavy simulations was not statistically significant [¢
(108) = 1.68, p = 0.1).

3.2.2 Pause Frequency. Figure 18 shows average
pause frequency (PF) for each participant in the Light
and the Heavy simulation. A visual inspection of the
data reveals a trend towards more frequent pauses in
the Light rather than in the Heavy simulation. Seven
of the 11 controllers show this pattern, and 3 show
the reverse pattern. Only Participant 11 shows con-
stancy in average pause frequency in both traffic
conditions.

To determine whether the observable trend to-
wards more frequent pausing during the Light simu-
lation was significant on a group basis, normalized
PF measures were generated. This was accomplished
by computing, for cach participant and cach PF
value, the ratio of that value to the participant’s mean
PF. A statistical comparison then was performed on
the normalized PFs from the Light and Heavy simu-
lations. The mean normalized PFs for the Light

10

and Heavy simulations were significantly differ-
ent [¢ (108) = 2.86, p = .05); controllers produced
more pauses during the Light simulation.

3.2.3 Pause Duration. Figure 19 shows average
pause duration (PD) for each participant in the Light
and the Heavy simulations. Again, there appears to be .
a trend towards longer pause durations in the Light
than in the Heavy simulation. Nine of the 11 partici-
pants show this pattern and 1 shows the reverse
pattern. Perticipant 7 shows non-discernable varia-
tion in average pause duration.

To determine whether the observable trend to-
wards longer pausing in the Light simulation was
significant ona group basis, normalized PD measures
were generated. This was accomplished by comput-
ing, for each participant and each PD value, the ratio
of that value to the participant's mean PD. A statis-
tical comparison then was performed on the normal-
ized PDs from the Light and Heavy simulations (1.46
and 0.54, respectively). The difference between the
mean normalized PDs was significant [t(108) = 2.85,
p = -05], controllers paused longer during the Light
simulation.

4.0 DISCUSSION

The data for this set of analyses contrasted acoustic
measures of communication generated by controllers
while they provided radar services to pilots on the
sector of their specialty (light traffic) and on a sector
other than their specialty (heavy traffic). All partici-
pants were full-performance journeymen controllers
who were highly skilled and knowledgeable about their
airspace and procedures. Workload was measured as the



exhaustive data collected by Eisler and her colleagues
show that individuals who are devoting fewer cogni-
tive resources to a discussion manifest shorter Type 2
pause durations than people thinking about what
they are communicating. The speech of the control-
lers in this study, therefore, may reflect a shift be-
tween a more cognitive “thinking” response mode in
Light traffic situations, where they know that they
have more time to respond, and a more automatic
mode that allows them to respond to the increased
pace induced by higher traffic loads. In other words,
we may be monitoring the degree to which the con-
trollers respond by means of reasoned, cognitive
rather than automatic, routinized responses.

Our research suggests that future investigation of
the speech of controllers should include measures of
hesitation as a measure of interest. Also, when con-
sidering potential acoustic-phonetic candidates for
inclusion in speech-derived measures of workload,
individual differences must be considered because of
the demonstrated variably in speaking among the
participants in this study. Thus, the results presented
in this report indicate that the examined speech
measures do not generalize across people and should
not be used to make inferences about groups. Speech
production is highly individualized and varies with
the speaker and situation.

5.0 REFERENCES

Absil, E., Grammatica, B., Harmegnies, B., Legros, C.,
Poch, D., and Ruiz, R. (1995). Time related
variabilities in stressed under laboratory
and real conditions. Proceedings of the ESCA -
NATO Tutorial and Research Workshop on S;
under Stress, (pp. 53-56). Portugal: Colibri,
Sociedade de Artes Graficas.

Atkinson, J. (1973). Aspects of intonation in speech:
Implications from an experimental study in voice
fundamental frequency. Ph.D. Dissertation, Uni-
versity of Connecticut.

Baum, S.R., Blumstein, S.E., Naeser, M.A., and
Palumbo, C.L. (1990). Temporal dimensions of
consonant and vowel production: An acoustic
and CT scan analysis of aphasic speech. Brain and
Language, 37, 327-338.

12

Benson, P. (1995). Analysis of the acoustic correlates of
stress from an operational aviation emergency.
Proceedings of the ESCA - NATO Tutorial and
Research Workshop on under Stress, (pp. 61-64).
Portugal: Colibri, Sociedade de Artes Graficas.

Blumstein, S.E., Cooper, W., Goodglass, H. Statlender,
H. and Gottleib, J. (1980). Production deficits in
ia: a voice-onset time analysis. Brain and
Language, 9, 153-170. ;
Borden, G.J., and Harris, K.S. (1984). Speech Sci-
ence Primer: Physiology, Acoustics, and Per-
ception of Speech, Second Edition. Baltimore:
Williams and Wilkins.

Coster, W.J. (1986). Aspects of voice and conversation
in behaviorally inhibited and uninhibited chil-
dren. Unpublished Ph.D, dissertation. (Harvard
University Archives HU90. 12239 Harvard
Depository).

Cummings, K.E., and Clements, M.A. (1990). Analy-
sis of glottal waveforms across stress styles.
Proceedings IEEE ICASSP (IEEE International
Conference on Acoustics, Speech, and Signal Pro-
cessing), (pp. 369-372). Piscatawya, NJ: IEEE
Service Center.

Eisler, F.G. (1968). Psycholinguistics: Experiments in
spontaneous speech. London: Academic Press.

Frick, R.W. (1985). Communicating emotion: The
tole of prosodic features. Psychological Bulletin,
97, 419-429.

Kagan, J., Reznick, J.S., and Snidman, N. (1988) Bio-
logical bases of childhood shyness. Science, 240,
167-171.

Kessinger, Be and Blumstein, S.E. (in submission).
Rate of speech effects on voice onset time in Thai,
French, and English.

Ladefoged, P. (1962). Elements of acoustic phonetics.
Chicago: The University of Chicago Press.
Lieberman, P, (1967). Intonation, perception and lan-

guage. Cambridge: MIT Press.

Lieberman, P. (1963). Some measures of the funda-
mental periodicity of normal and pathologic la-
synges. Journal of the Acoustical Society of America,
35, 344-353. ~



Technical Report Documentation Page

1. Report No. ‘2. Government Accession No. 3. Recipieat’s Catalog No.
DOT/EAA/AM-98/20
7 The nd Subtae 3. Report Dete
An Acoustic Analysis of ATC Communication July 1998
6. Pecfocssing Organization Code
7. haborts) 2 Redan Outi Rape
Prinzo, O.V.', Lieberman, P., and Pickert, E.”
9. Performing Organization Name and Address 70. Work Unit No. (TRAIS)
‘PAA Civil Aeromedical Institute | *Brown University
P.O. Box 25082 Providence, RI 02912

Oklahoma City, OK 73125

11. Commact or Grant No.

800 Independence Ave. S.W.
Washington, D.C. 20591

13. ‘Type of Report and Period Covered

14, Sponsoring Ageacy Code

15. Supplemental Nowe
This work was performed under Task AM-D-96-HRR-513

16, Abswact

This report consists of an acoustic analysis of air traffic control (ATC) communications. Air traffic control specialists
(ATCS) from a TRACON facility participated in the simulation study. Each ATCS worked light and heavy traffic :
density scenarios for 2 feeders and 1 final sector. Ail communications were audio recorded and transcribed verbatim by a
retired ATCS. Workload was determined by the number of aircraft under positive control when the ATCS initiated a
transmission. Utterances were selected to achieve maximal workload contrast. For each participant, the 5 lowest
workload utterances from the Light version of the scenario (simulating that participant’s normal work station) and the 5
highest workload utterances from the Heavy version of the scenario (simulating a work station unfamiliar to the
participant) were identified and digitized. For all participants, speaking rate (syllables/second), pause frequency (number
of pauses/number of words), and pause duration (duration of patises/number of words) were generated from the selected
utterances using the BLISS speech analysis system (Lieberman and Blumstein, 1988). The results indicate that ATCSs
tended to pause more frequently and for greater duration under a light workload condition. The hesitations found in
their speech may reflect a shift between a more cognitive “thinking” response mode in light traffic situations where
ATCSs know that they have more time to respond and a more automatic mode, which allows them to respond to the
increased pace induced by higher traffic loads. In condusion, it appears that hesitation in speech may be a potential
indicator of workload. Despite its highly speaker-dependent nature, hesitation pauses may be a useful indicator of an
ATCS's responding in a cognitive, rather than in an automatic mode.

17. Key Words 78. Distibetion Sutemeat
ATC Workload Document is available to the public through the
Acoustic Analysis National Technical Information Service
ATC Communications Springfield, Virginia 22161
19. Seceriey Classif. (ofthis seport) 2B. Security Clesit, (of is page) 21. No. of Pages 2. Paice
Unclassified Unciassi 27
‘Reprodoction of completed page wethorized

Form DOT F 1708.7 (8-72)

i



8.00

Might
Cot

7.00 4

6.00

5.00

4.00

3.00

Syllables per Second

2.00 4

1.00 4

0.00
ah 2

Figure 17. ‘Average Speaking Rate (SR) for Each Participant in the Light and the Heavy Conditions.

O18

0.16 4

0.144

0.124

0.10

0.08 4

0.06 4

0.04 4

Number of Pauses / Number of Words

0.02 4

0.00

Figure 18. Average Pause Frequency for Each Participant in the Light and the Heavy Conditions.

A-9


NOTICE

This document is disseminated under the sponsorship of
the U.S. Department of Transportation in the interest of
information exchange. The United States Government
assumes no liability for the contents thereof.



waveform. A pause was defined to be a “flat” portion
of the waveform greater than 25 ms. Some articula-
tory gestures of speech (e.g, stop consonants) neces-
sarily result in brief periods of silence. Using a 25 ms.
lower bound excludes these articulatory factors. Pause
duration (duration of pauses/number of words) was
measured by placing cursors at the onset and offset of
silence, as indicated by flattening of the waveform.

The comprehensive analysis of Participant 1’s
speech included 3 additional measures. For each
utterance, an FO track, or pitch track was computed
for the entire utterance. Pitch analysis was done by
use of the Short-Term Autocorrelation algorithm.
Fundamental frequency is the lowest harmonic in the
Fourier decomposition of a complex waveform. The
Autocorrelation method extracts this harmonic from
the waveform (Lieberman and Blumstein, 1988).
The resulting pitch tracks were analyzed using an
interleavings and offsets method, in which individual
pitch tracks are interleaved and the spread, or offset,
is assessed.

Additional measurements were performed on the
subset of utterances that included the words “contact
approach,” when found in the utterance-final posi-
tion (26% of utterances). This data set was chosen to
minimize variation attributed to context, both lexical

and phrasal. VOTs were measured for the voiceless
velar stop [k] at the beginning of the word “contact”
and the voiceless alveolar stop [t] at the beginning of
the syllable “-tact.” Cursors were placed at the onset
of the burst produced at the release of the each stop
consonant and at the onset of phonation, by means of
both visual inspection of the waveform and by listen-
ing to marked portions of the signal. The duration of
the pitch period (i.e., a single opening and closing of
the vocal folds, as described above in Section 1.1) of
the highest amplitude portion of the vowel in the
syllable “-proach” is depicted in Figure 4. It was
measured by placing cursors on 2 successive peaks of
the waveform.

3.0 RESULTS

3.1 Narrow Focus, Participant 1

Three hundred and thirty-three utterances pro-
duced by Participant 1 make up the data set in this
section (150 utterances East Heavy scenario, 183
West Heavy scenario). Reliable relationships between
the utterance measures and workload for each sce-
nario were examined. No direct statistical compari-
sons between East and West data were performed.

227.55 |

eae ST]

[orn futsten| open | coro | eat

Figure 4. BLISS system display showing the waveform of the
syll

lable “proach.”

ursors LO and RO mark a single pitch period from

the high amplitude portion of the vowel.


number of active aircraft on frequency at the moment
a controller initiated an utterance. To maximize the
likelihood of significant results between workload
and the selected acoustic measures, the 5 utterances
transmitted under the lightest and heaviest workload
simulations were examined for 11 of the 12 control-
lers. A very detailed and complex set of acoustic
analyses was performed only on the data from 1
controller.

The results presented here suggest 2 points of
interest. First, as a group, the ATCSs who partici-
pated in this simulation study displayed a tendency
both to pause more frequently and pause longer
during a light rather than heavier workload situation.
From these results, it is possible to infer that the type
of “hesitation” produced by this group of controllers
is not associated with factors such as task difficulty,
as described above in Section 3. Instead, these data
may reflect the possibility that, when workload is
light, controllers may attend to the task in hand using
a “cognitive” rather than an “automatic” response
mode. Under a light traffic load, controllers had
more flexibility and latitude in determining runway
assignments and sequencing aircraft for the approach.
Light traffic coupled with the expertise of working on
their own sector specialty allowed for more thinking
time, especially when the constraints imposed by
rapidly converging aircraft into a small airspace were
removed.

At busy Level 5 TRACON facilities, standard
terminal approaches are used, and pilots know that at
particular locations they must have their aircraft ata
particular altitude, heading, and airspeed. As part of
their training, controllers learn when to descend,
slow, turn, and clear an aircraft for an approach; they
also learn when and how to transfer radar and radio
communication to the next controller in the se-
quence. Since they deliver this information over and
over again, hesitations diminish. Under heavy traffic,
a more highly automatic, routinized approach to
traffic management became operational, and com-
munication vith pilots became “canned,” and repeti-
tive. Under periods of heavy workload, more
routinized cognitive processes might occur, as dem-
onstrated by fewer pauses of shorter duration. This
possibility is likely in light of previous studies of
pause duration and frequency (Eisler 1968) and given
the lack of a statistically significant change in speak-
ing rate.

il

It is especially interesting that both measures of
hesitation increased. The measures used are, in prin-
ciple, independent of one another. That is, because
pause duration is averaged across all pauses in an
utterance, there is no 4 prigri reason to suppose that
an increase in the number of pauses would be associ-
ated with an increase in the length of those pauses.
And, in fact, in some cases ATCSs who showed the
dominant trend on one of these measures showed the
opposite of the dominant trend on the other measure.
Only 55% participants showed both trends, as dis-
played in Figures 18 - 19.

The Light simulation utterances were on average
slightly longer in duration (3218 msvs. 3027 ms) and
slightly greater in number of syllables spoken (18.6
vs. 16.6). Although these differences were not statis-
tically significant, it may be the case that “more
speech” provides more opportunity both for more
frequent and longer pauses. However, the occurrence
of both longer Type 2 normalized pause durations
and normalized pause frequencies is consistent with
the controllers responding in a more cognitive mode
under the Light condition.

The second point of interest is that while there
were strong group effects for the 2 measures of
hesitation, these effects were rarely significant on an
individual basis. Further, regardless of the size of the
effects, none of the 3 measures showed trends in the
same direction for all participants. This was espe-
cially true for speaking rate, for which 2 participants
showed significant differences in opposite directions.

The importance of this fact, the variability in
speaking among participants, is highlighted by the
results from the more in-depth analysis of the speech
of Participant 1. Despite the analysis of more than
300 utterances, only 1 significant, albeit weak corre-
lation was found between workload and the many
acoustic measures. We conclude this report by sug-
gesting that the data from Participant 1 may not be
representative of the pool of data provided by the
other 11 participants.

The results of analyses performed for each acoustic
measure and workload lead us to conclude that hesi-
tations found in speech may be a potential indicator
of workload, as measured by pause duvation and
pause frequency, in particular. Despite its highly
speaker-dependent nature, hesitation may prove to
be a useful indicator of a controller's responding in a
cognitive, rather than in an automatic mode. The