the effect of interpreter fatigue on interpretation quality · interpreting task. dean and pollard...

The Effect of Interpreter FatigueOn Interpretation Quality

Jessica GabrianGerard Williams

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Introduction! 4

Review of the Literature ! 6

Interpreter Fatigue! 6

Physical Fatigue! 7

Interpretation Quality! 8

Methodology! 9

Recruitment! 9

Subject Description! 10

Source Materials! 10

Videotaping! 11

Transcribing! 13

Data Analysis- Omissions! 13

Data Analysis- Processing Time! 16

Data Analysis- Language Production Rates! 17

Results ! 18

Omissions! 18

Processing Time! 20

Language Production Rate! 22

Interpretation of Results ! 24

Limitations ! 26

Conclusions! 28

References ! 30

Appendix! 32

Tables- Production Rates! 32

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Tables- Processing Time Analysis! 33

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Introduction

The act of interpretation is an extremely complicated, multi-stage cognitive activity

demanding great concentration on the part of the interpreter. Interpreters simultaneously manage

internal factors (distraction, translation) and external factors such as their physical comfort,

ability to hear or see the speaker, and access to the audience, all while tenaciously attending the

speaker. The results of this arduous effort are a production of the speaker's original message in

another language and exhaustion for the interpreter performing the task.

Interpreters of spoken languages have traditionally worked consecutively, wherein they

attend the speaker exclusively until they have an idea or message, then render their interpretation

during a pause in the discourse. By separating the attending and production steps, interpreters

working consecutively have effectively reduced the density of their cognitive load; that is, they

are doing the same amount of work but they are doing it over a longer period of time.

Consecutive interpretation was the standard practice in the spoken language interpreting field

until the presence of so many different language speakers at a single trial led to the

implementation of technological advances to allow for non-intrusive audio interpreting

simultaneous with the speaker. (Seleskovitch, 1994). Overall, spoken language interpreters have

historically worked consecutively not because of the intense cognitive processes at work, but

rather due to the conflicting nature of dual auditory input. Simply put, two people speaking at the

same time is distracting, and in spoken languages typically one speaker speaks at a time.

When interpreting from a signed language into a spoken language or vice versa, this

conflicting modality ostensibly ceases to be an issue. Both Deaf and hearing consumers perceive

a single linguistic input (either spoken or signed) at any given time, leaving no apparent

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logistical reason to preclude simultaneous interpretation. Thus, to a great degree the standard of

practice has been to use simultaneous interpreting, and it has become generally accepted as the

most common form of sign language interpretation. When interpreting simultaneously, the same

cognitive load is distributed over a smaller time frame, resulting in an increase in demand on the

interpreter's mind.

Gile (1995 p. 161-162) proposes the effort model of interpreting, which presumes that

interpreters have a limited amount of cognitive power available at any given time. It is also

presumed that as a complex multiphase cognitive activity, interpreting demands most if not all

available cognitive power. This power is distributed among the various tasks an interpreter is

engaged in, and any increase in the cognitive load for a particular task without a corresponding

decrease in load for another will result in a miscue or interpreting error. Considering both the

effort model in interpreting, and the relative complexity of the simultaneous interpreting task, it

is not surprising that interpreters work in teams. Not only does this allow interpreters to make

use of other, external cognitive resources (e.g. asking a team member for a feed or alternate

interpretation), but when the pressure on the interpreter's mind becomes too great and they begin

to become fatigued, the co-interpreter (who has presumably been operating at less than their

cognitive capacity) can replace them. The industry standard for this switching of interpreters is at

twenty minute intervals in order to reduce the impact of this interpreter fatigue upon the quality

of the interpreting product, as well to safeguard the health and well-being of interpreters.

Given the understanding of the high demand on interpreters and the prevalence of injury

and fatigue due to sustained interpreting, and in light of the twenty minute intervals for team

interpreters to switch out, it seems paradoxical to consider that often interpreting assignments up

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to 119 minutes may be covered only by a single interpreter. Only for assignments exceeding the

two hour time limit will two interpreters be absolutely required (and in some cases, even then

there may be but one interpreter hired due to budgetary constraints or lack of awareness). This

convention in the field begs the question, what is the impact of an interpreter's fatigue on the

interpretation? How does the interpreter's fatigue manifest itself, and when?

Review of the Literature

Interpreter Fatigue

The seminal work on interpreter fatigue was conducted in 1976 by Brasel. Five

interpreters were recruited to participate in the study, then chosen at random to interpret into

American Sign Language (ASL) from an English audiotape for 20, 30, 60, or 90 minutes. Each

subject was given a battery of cognitive and performance tests both prior to and after

interpreting, such as typing tests, memory tests and trigram tests. The interpretation itself was

analyzed simultaneously by a panel of judges that included two deaf people and two interpreters.

The Deaf judges made notes of any errors during the interpretation, including:

"..slurring of fingerspelling, use of the wrong sign for a word, failure to keep up with the speaker, "bootleg rests," or any time the deaf person found himself baffled or confused or did not understand a fingerspelled or signed word" (p. 20)

The hearing judges (interpreters) noted not only the above, but were also "to watch the

interpreter for concepts he failed to convey or misinterpreted." (p. 20) These errors were

examined in conjunction with the time period, and the interpreter was given a rating on a scale of

1-5 (with one being negative and five being outstanding). Ultimately it was determined that

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interpreting skills will continue to significantly deteriorate after a thirty minute uninterrupted

interpreting task.

Dean and Pollard (2005) discuss occupational stress in the interpreting profession by

applying the demand control schema to the interpreting task. This model enumerates the

intrapersonal, interpersonal, environment, and linguistic demands on the interpreter, then

compares this myriad of demands with the amount of control available to the interpreter over

these factors. This model proposes that a high demand interpreting assignment with low control

factors will result in increased stress on the interpreter, and may impact the burnout rate in the

profession.

Physical Fatigue

Sign language interpreting is uniquely positioned among sociolinguistic professions in

that it involves, by definition, some amount of physical exertion. From an occupational health

perspective, numerous quantitative analytic studies have been conducted to determine the exact

impact of sustained ASL interpretation on the physical health of the interpreter (e.g. Repetitive

Motion Injury, Cumulative Motion Injury, Carpal Tunnel Syndrome, etc.), and the prevalence of

chronic injury from such exposure (Desile et al., 2005; Fitzgerald and Feuerstein, 1992; Madden,

n.d.;Podhorodecki and Spielholz, 1993; Registry of Interpreters for the Deaf, 1997; Stedt, 1992).

Johnson and Feuerstein (2005) reviewed the open ended responses to a survey of RID

interpreters with regards to musculoskeletal disorders, specifically those related to cause of,

prevention and treatment of such disorders. Results showed that one of the factors rated highly to

contribute to such disorders was "long work hours with few or no breaks" (p. 409). One of the

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common responses to the onset of symptoms is was "reducing interpreting load by lessening

work or including a team interpreter" (p. 410).

Interpretation Quality

Cokely (1992) recorded, transcribed, and analyzed the naturalistic interpretations of six

interpreters at the Conference of Interpreter Trainers (CIT) in Monterey California working from

English to ASL. Cokely defines five general miscue types that are subsequently analyzed in the

study: omissions, additions, substitutions, intrusions, and anomalies. In addition to miscue

analysis, Cokely calculated the adjusted (excluding pause-time) and unadjusted (including pause

time) average language production rates for both each interpreter and each presenter. Onset

Processing Time time was also analyzed by comparing the beginning of a source language (sL)

sentence and a target language (tL) sentence. Utilizing temporal data (onset Processing Time

time, language production rates, pause times) Cokely concluded that the interpreter is managing

a wide range of cognitive demands throughout the interpreting task, despite the prevalence of

pauses in the sL. Considering temporal data in tandem with miscue data, Cokely also concluded

that there is an inverse relationship between Processing Time time and frequency of miscues (i.e.

shorter Processing Time time will result in more miscues), and that longer Processing Time time

is correlated with more native-like language production.

Napier (2003) also analyzed interpretation quality by recruiting ten Australian Sign

Language (Auslan) interpreters to participate in a controlled experiment designed to analyze

omissions from a sociolinguistic perspective. Each participant watched ten minutes of a recorded

English lecture, then interpreted the subsequent twenty minutes into Auslan. The researcher was

present during the session with a transcript of the source language data, in which notes were

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made when omissions occurred. For the purposes of the study, the following definition of

omissions was adopted: "An omission occurs when information transmitted in the source

language with one or more lexical items does not appear in the target language and, therefore,

potentially alters the meaning" (p. 124).

Following the interpretation, the researcher and interpreter reviewed the videotape of the

interpretation, pausing to discuss omissions when they occurred. In light of data gleaned during

this collaborative review, omissions were categorized as one of five types: conscious strategic,

conscious intentional, conscious unintentional, conscious receptive, and unconscious omissions.

Interpreters were also engaged in a more broad discussion of their interpreting strategies,

educational background, and feelings about the recent work. The taxonomy of omissions clearly

shows that interpreters consciously utilize omissions as a strategy while interpreting dense

academic lectures, and generally the frequency of omissions was positively correlated with the

density of the text.

Methodology

Recruitment

Due to the time constraints involved with this research project, a case study method was

adopted in order to focus more intensively on a single interpreter. In order to participate in this

study, the interpreter was required to have over five years of professional experience and

credentials from a national certifying body. The certification held by the interpreter could have

been the National Interpreter Certification (NIC), Certificate of Interpretation (CI) or National

Association of the Deaf (NAD) Levels IV-V.

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This information was included explicitly on the fliers which were used for interpreter

recruitment purposes, along with a description of what participation in this study would entail.

The potential participants were told that they would be asked to interpret from ASL to English

and English to ASL for a period of 90 minutes on two separate occasions and be compensated at

a rate of $40 an hour for their time, which is a competitive rate in the interpreting field. The

recruitment posters for this study were posted around the Gallaudet University Campus and in

the Gallaudet Interpreting Services office. Those who were interested in joining the study were

asked to notify the researchers via an anonymous email set up for this study. The interpreter's

certification status was verified by the Registry of Interpreters for the Deaf's (RID) website.

Subject Description

Within a short period of time, a potential participant contacted the research team and

expressed interest in being involved with the study. The researchers were able to verify that the

interpreter does indeed hold certification from one of the aforementioned certifying bodies. The

participant has been working professionally since September 2002. The interpreter participant is

not a child of a deaf adult (CODA) and is a native speaker of English. The length of time spent

learning American Sign Language was not disclosed by this particular interpreter. Although

never formally trained in interpreting, the participant was currently pursuing a Bachelor's Degree

in Deaf Studies at Gallaudet University.

Source Materials

The source materials for this project were collected from Gallaudet's Department of

Interpretation's library. Each of the source materials were academic orations from the

Department Of Interpretation's Lecture Series. The English presentation was given by Dr. Heidi

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Hamilton, professor of Linguistics at Georgetown University, at Gallaudet University during the

fall semester of 2005. Dr. Hamilton's lecture entitled "Symptoms and Signs in Particular: The

Influence of the Medical Concern on the Slope of Physician-Patient Talk" was one hour and 23

minutes in length. The pre-recorded lecture will be used for the session in which ASL is the

target language.

The goal was to obtain and utilize a lecture of with a comparable level of sophisticated

language use and length found in the lecture given by Dr. Hamilton that was also conducted in

ASL. The ASL source material was a DVD of Dr. Paul Dudis's lecture entitled "Depiction in

ASL" and was used for the session in which English was the target language to be used by the

interpreter. "Depiction in ASL" was presented during the Lecture Series held in the spring

semester of 2007. This source text was one hour and 21 minutes in length. The participant was

provided with a copy of the PowerPoint presentations from both of the source lectures twenty-

four hours prior to the filming sessions to prepare.

Videotaping

The first data collection session was English to ASL, and took place in the Department of

Interpretation's undergraduate classroom on Gallaudet University's campus. A television media

cart was used to play the source DVD, and the screen was oriented parallel to the interpreter

towards the cameras, as if she were interpreting with a presenter beside her. The volume was set

to meet the needs of the interpreter prior to filming. The video cameras were placed in a position

that allowed both the interpreter and the television to be captured on film. The interpreter used a

podium to be able to refer to her copy of the PowerPoint presentation during the interpretation.

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The second portion of the data collection took place four days after the initial filming

session in the Department of Interpretation's MA Classroom. In this session the television

volume was muted, and the interpreter sat facing the television. Both cameras were oriented at

the television during the interpretation, recording the ASL source, and captured the interpreter's

voice via the built in microphones. The interpreter was not on camera at all during the session,

with the exception of one time that she stood to adjust the air conditioning in the room and

walked through the camera's path. One of the researchers was present at both sessions to set up

the cameras and ensure that volume levels and picture quality were optimal for the interpreter,

but did not take notes before, during, or after the interpretation.

Both the English to ASL and ASL to English interpreting sessions were recorded using

two camcorders, each with a 60 minute miniDV tape. The cameras were operated in series (as

opposed to parallel) so that the entire interpretation would be captured on tape. All four tapes

were imported to iMovie, where they were exported to Quicktime format (.mov). Movie files

corresponding to the same session were then merged using Quicktime Pro, and time-codes were

added to the merged video using QT Sync. Those time-codes were then used to edit the video

into four non-consecutive five minute sections (00:10:00:00-00:15:00:00,

00:30:00:00-00:35:00:00, 00:50:00:00-00:55:00:00, 01:14:00:00-01:19:00:00). Data DVDs were

burned and provided to the transcribers including all four clips from each session, a complete full

length copy of the session, and a ReadMe PDF file including a table of contents to the clips and

transcription instructions.

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Transcribing

Both video recordings were transcribed by graduate research assistants, then merged

with transcripts of the source material. Both transcribers utilized a very broad method of

transcription. Details such as eye gaze, rhetorical questions, mouth morphemes and phonological

variation were not included or necessary for the purpose of this research. Transcripts were

merged in thirty second increments, with two thirty second sections of the source and target

transcripts per page (See Figure 1 below). The transcripts were numbered according to session

(Session 1: English-ASL and Session 2: ASL-English) and clip number (1-4), for a total of eight

transcripts (see Appendix). The transcripts were then annotated by the researchers to highlight

omissions and determine anchors for processing time analysis.

Data Analysis- Omissions

In order to determine the effects the interpreter's fatigue had on the quality of the

interpretation, three separate linguistic analyses were conducted. The analysis methods were

outlined in Dennis Cokely's dissertation work. It was hypothesized that the interpreter's fatigue

would have a direct correlation with the amount of omissions in the target language and the

amount of processing time the interpreter used. More specifically, a fatigued interpreter might

show a decreased amount of processing time, which in turn would lead to a higher rate of

omissions in the target language. Upon reviewing the videotapes, both researchers and

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Figu

re 1

transcribers noticed a qualitative difference in the interpreter's language production in clips later

in the session. It was then hypothesized that this may be reflected in the rate of language

production on the part of the interpreter (i.e. words per minute (WPM), signs per minute (SPM)).

For the purposes of this study, analysis was limited to only the number of omissions in

the tL interpretation, excluding other types of miscues (addition, substitution, anomalies, and

intrusions). Cokely (1992), states that "information contained in the sL message may be left out

of the tL message" (p. 74). This definition of omission was used as the guideline for identifying

information that was not present in the target language. Similar to Cokely's approach, this study

did not take false starts or vocalized pauses (e.g. 'um' or 'uh') into account when the number of

omissions were calculated.

Once Cokely recognized the broader category of omissions, it was divided into

morphological, lexical and cohesive omissions (Cokely, p. 77-78). Morphological omissions

occur when information that is stated by the use of 'bound morphemes' in the sL is omitted in the

tL. A lexical omission is defined as "the omission of content information that is clearly conveyed

by distinct lexical items or phrases in the sL message" (Cokely, p. 77). This type of omission was

the most applicable to this study therefore was the only kind of omission that was pinpointed

during the miscue analysis. Finally, "cohesive omission refers to the omission of the

informational and/or functional value of an item in the sL text that can only be determined by

reference to or relation with a preceding item in the sL text" (Cokely, p. 78).

While Cokely was able to outline three types of omissions, research that was conducted

later by Napier (2003) further examined the category of omissions. Through data collection and

an interview with the interpreters after the interpreted session, Napier was able to identify five

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types of omissions that occur in an interpretation. The five categories are conscious strategic,

conscious intentional, conscious unintentional, conscious receptive, and unconscious omissions.

Due to nature of these classifications, an interview with the interpreter is required after the

interpreted interaction to determine whether the omitted work was conscious, intentional or

unconscious. This particular study on the effects of interpreter fatigue did not incorporate a

follow-up interview into the methodology. While Napier's (2003) research was highly significant

in the field of interpretation, it was not directly applicable to this project.

For both of the interpreted sessions, the transcripts of each five-minute clip were used to

identify the lexical items that were omitted in the target language. Each of the researchers were

careful to inspect the source language and target language transcripts to detect lexical items that

were not accounted for in the target language interpretation. Even though there are words or

signs that have no translated equivalent in the respective target language, they still have meaning

that should be conveyed in the interpretation. Each lexical item was equivalent to one omission.

For example, if the English utterance 'how, uh, how difficult is it?' was omitted from the target

language it would count as five lexical omissions. Again, the vocalized pause 'uh' is not included

as a lexical item.

This process was slightly more complicated when examining the lexical omissions in the

ASL to English interpreted session. This first step was to clearly define what would constitute a

lexical item in ASL. The three specific challenges when identifying a lexical item in ASL are the

reduplication of signs, the use of depicting verbs and fingerspelled items. It is possible that these

questions have addressed in previous research. However, in order to be mindful of the time

constraints on this project, it was decided that each transcribed gloss in ASL would equate to one

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lexical item. For example, a fingerspelled term would count as one item as opposed to counting

each letter as an item. Also, if one sign was produced more than once immediately after the

initial production, it was only counted as one lexical item in ASL. Finally, the particular lecture

that was used for this study was rich with depicting verbs. The same theory was applied to the

depicting verbs; one gloss equaled one lexical item. More specifically, if the ASL utterance IX

(self) LOOKING-AT-CARDS was not conveyed in the target language, it would amount to two

lexical items. The dashes used in between the words LOOKING AT CARDS indicates that they

are grouped together as a depicting verb, therefore they can be considered as one lexical item.

While inspecting the transcripts for lexical omissions, each one that was found was

marked with a yellow highlighter. This process was repeated twice by both researchers to ensure

that any omissions were not overlooked. When all the omissions in the entire five minute clip

were identified, they were tallied and recorded. After all four clips were analyzed for omissions,

they were arranged into a table to be compared.

Data Analysis- Processing Time

In order to determine processing time, the researchers analyzed the completed merged

transcripts for occurrences of "anchors" in the sL and tL. Ideal anchors are proper nouns (names,

places, titles) or numbers (percentages, years) which are both essential to the sense of the

message and will have a direct equivalent in the tL. For example, see the following excerpt from

transcript 1.1.

In the example above, the lexical item “Roberts” was used as an anchor. The time-code

(hh:mm:ss:ff) for each instance of that lexical item was recorded for both the sL and tL, as

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measured by the end of the item. The difference between the two time-codes was then calculated

in seconds and tenths of a second, converting frame number according to the frame-rate of the

source video in which one frame equals one thirtieth of a second. This processing time was

calculated for all identified anchors in each five minute clip, then averaged.

Data Analysis- Language Production Rates

Following Cokely's (1992) analysis of language production rates, the researchers

performed a count of lexical items in both the sL and tL for each five minute clip. It is important

to note that for ASL utterances (either sL or tL), single item reduplications were not counted. For

example, the gloss EVERYDAY++++ indicates that the sign was repeated four times

consecutively, but for the purposes of this analysis this was counted as a single lexical item. Due

to the unique nature of reduplication in ASL, it was thought that inclusion of these reduplications

in the lexical item count would result in an unrealistically inflated rate.

Also of note is the unique nature of sign language it is possible to represent more than

one visible element simultaneously. In these cases the simultaneous signs are transcribed

consecutively (e.g. (lh) SIGN (rh) SIGN) and as such are counted as two signs, but this signing

did not occur over a span of time. Beyond that, depicting verbs (historically referred to as

classifiers) portray a wealth of spatial, temporal, and grammatical information distributed across

both hands as well as the signer's body and face. For the purposes of this research, depicting

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Figu

re 2

verbs have been glossed in a simplified manner (e.g. DV: CAR-DRIVING-UP-HILL), and so

each instance of a DV construction has been counted as a single lexical item.

The total number of lexical items produced for both the sL and tL for each clip was then

divided by the length of that clip in minutes (for all clips, the length was 5 minutes) to derive the

average WPM or SPM rate. These rates were not adjusted to account for time that the speaker or

interpreter spent pausing, due to limitations of time to complete the research. Both speaker and

interpreter rates were calculated in order to ensure that a spike or dip in the interpreter's

production rate would not be mistakenly attributed to fatigue when, in fact, it correlated with a

spike or dip in the speaker's production rate.

Results

Omissions

Omission counts for Session 1 (English-ASL), separated by clip number, are show in

Table 1 below. There is a clear linear relationship between time (as represented by clip number)

and number of omissions. The R2 value for the linear regression represented by this data is .7786,

indicating a strong probability that the trend line (shown in blue on Figure 1) would accurately

predict the values. From Clip 1 to Clip 2 there is an 81% increase in omissions, and from Clip 2

to 3 there is a 37% increase in omissions. The difference in omissions from Clip 3 to Clip 4

(<1%) is negligible, although it is interesting to note that omissions seem to plateau at that point.

Omissions Analysis: Session 1 (English-ASL)

Omission CountClip 1 43

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Omission CountClip 2Clip 3Clip 4

7810798

The omissions data for Session 2 (ASL-English) is represented below in Table 2. Among

the first three clips there is a maximum change in omissions of 18% (between Clip 2 and Clip 3),

but between Clip 3 and Clip 4 there is a precipitous 211% increase. The R2 value for a linear

trend line in this data is .6231, indicating a moderate correlation, however if we consider the

count for the final clip as an outlier and remove it from the data, this value drops to .1967. It is

interesting to note that the omissions counts for the first three clips of Session 2 start out

approximately where the omissions count for Session 1 peaked and plateaued.

Omissions Analysis: Session 2 (ASL-English)

0

30

60

90

120

Clip 1 Clip 2 Clip 3 Clip 4

98107

78

43

R² = 0.7786

Omission Count

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Tabl

e 1

Cha

rt 1

Omission CountClip 1Clip 2Clip 3Clip 4

110100118367

Processing Time

Processing time analysis for Session 1 (English-ASL) is shown below in Table 3. The R2

value for a linear trend line of this data is .4102 (see Figure 3), indicating a fairly low possibility

that this trend accurately describes the data. Tables containing the lexical items used to anchor

this analysis and a breakdown of the calculations of average Processing Time times per clip are

included for both sessions in the Appendix.

0

100

200

300

400


R² = 0.6231

Omission Count

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Cha

rt 2

Tabl

e 2

Processing Time Analysis: Session 1 (English-ASL)

Average Processing Time (seconds)

Clip 1

Clip 2

Clip 3

Clip 4

2.4

2.9

2.8

2.8

Processing time analysis for Session 2 (ASL-English) is shown below in Table 4. Again,

Clip 4 stands out in this session as having the highest processing time, as well as the most

omissions (Table 2). The trend for this data is increasing, with a R2 value of .7847, but again if

this outlier is removed, the R2 value drops significantly to .5714.

Processing Time Analysis: Session 2 (ASL-English)

0

0.725

1.450

2.175

2.900


2.82.82.9

2.4

R² = 0.4102


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Tabl

e 3

Cha

rt 3


Clip 1

Clip 2

Clip 3

Clip 4

2.4

2.9

2.8

4.0

Language Production Rate

Language production rate analysis for Session 1 (English-ASL) is included below in

Table 5. Complete tables showing calculation of average language production rates can be found

in the appendix. This data does not show a significant trend in either the speaker or interpreter’s

language production rates (R2=.0756 and .0381 respectively).

0

1

2

3

4


4.0

2.82.92.4

R² = 0.7847


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Tabl

e 4

Cha

rt 4

Language Production Rate Analysis: Session 1 (English-ASL)

Source Target

Avg Words Per MInute (WPM)

Avg Signs Per Minute (SPM)

Clip 1

Clip 2

Clip 3

Clip 4

160.2 90.6

147.4 89

167.6 94.2

159.4 87.4

Language production rate analysis for Session 1 (ASL-English) is included below in

Table 6. Although the trend line for the interpreter’s production rate seems to clearly decline

(R2=.5062), this data is again impacted by the fourth clip. While the source language production

rate increased 35% from Clip 3 to Clip 4, the interpreter’s production rate decreased 44%.

Excluding this data again significantly decreases the reliability of the trend line (R2=.0163).

0

42.5

85.0

127.5

170.0


R² = 0.0381

R² = 0.0756

Words Per MInute (WPM) Signs Per Minute (SPM)

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Tabl

e 5

Cha

rt 5

Language Production Rate Analysis: Session 2 (ASL-English)

Source TargetSigns Per

Minute (SPM)Words Per

MInute (WPM)Clip 1Clip 2Clip 3Clip 4

96.6 12982.2 112.879.6 131.6

107.6 73.4

Interpretation of Results

The most compelling result from our analysis is the progressively increasing number of

omissions in the English-ASL session. The fact that the number of omissions steadily increases

over time (as represented by clip number) seems to be a clear indication that the interpreter’s

mental or physical fatigue is coming into play. This data corroborates the earlier research by

Brasel (1976) which indicated that there was a significant decrease in quality of the interpretation

after 30 consecutive minutes. It is also interesting to note that the omissions spike in Clip 3 and

0

35

70

105

140


R² = 0.5062

R² = 0.09

Signs Per Minute (SPM) Words Per MInute (WPM)

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Tabl

e 6

Cha

rt 6

in fact drop slightly in Clip 4, indicating a plateau phenomenon. The progressive increase trend is

not reflected in the ASL-English data, but a similar plateau seems to be manifested in Clips 1-3

of the Session 2. Omissions in clips 1-3 of the ASL-English data seem to hover slightly above

100, in the same way as clips 3-4 of the English-ASL data. It may very well be that there is some

limit to the number of omissions that may occur in an interpretation (or at least for a particular

interpreter), and that in the ASL-English data this quota was reached much sooner.

There is clearly something extraordinary about Clip 4 of the ASL-English session, as

shown in omission counts, language production rates, and processing time. This last clip of the

ASL lecture also included questions from audience members which seemed to be particularly

challenging to the interpreter, and this may have impacted the interpretation. However, without

any other data to compare this with (i.e. Other interpreters) it is impossible to draw conclusions

about what occurred to cause the unprecedented increase in omissions and processing time, and

the decrease in language production rate.

The language production rate data overall does not indicate a trend that could be

attributed to fatigue, or indeed, any trend at all. It is possible that if time had allowed for

calculation of average language production rates excluding pause information that there may

have been a more meaningful pattern. Qualitatively however, both the researchers and the

research assistants took note of some change in the interpreter’s language production and

prosody in the later clips of each session. Further analysis of these clips is warranted to

determine the exact source of this observation.

The clear increase in omissions from English-ASL when compared with the rather erratic

omissions in ASL-English implies a fundamental difference in these two sessions. In the first, the

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interpreter is able to use coding/transliteration as a strategy to deal with information that he may

not understand, and as such omissions increase gradually as fatigue increases. However, when

working from ASL-English the interpreter does not have this luxury. Analysis of syntactic use in

the English-ASL session may bring the differences in the two sessions into more clear relief.

Limitations

While prerecorded materials are commonly used in interpreting research and interpreter

training programs, the artificial setting will inevitably effect the quality or naturalness of the

interpretation. Interpreters operate differently in face-to-face situations. For example, in a

naturalistic setting an interpreter would have the opportunity to request clarification with the

speaker if necessary. However, this was not possible during the ASL to English and English to

ASL sessions used for this research. Further, the dense academic lectures used as source material

are unlikely to be interpreted in the field by only a single interpreter. In fact, two interpreters are

included in the video of Dr. Hamilton’s lecture, relieving each other nearly every twenty minutes.

In light of this, it would certainly be more representative of the real world effect of fatigue to

record naturalistic interpreted interactions and situations that are attended by only a single

interpreter.

Transcription of both source language utterances and target language renditions for a total

of forty minutes of interpreting is extremely time consuming. Due to the constraints of time, we

were forced to limit our study to a single interpreter. As a result, we cannot draw general

conclusions about the effect of fatigue, but rather can discuss the possible impact of fatigue on

this interpreter in this situation. Future studies would benefit from a pool of similarly qualified

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interpreters, in order to minimize the impact of a single skew in the data (as in this study, Session

2 Clip 4).

Initially, we were able to hire native language users of English and ASL to transcribe the

discourse in the English and ASL sessions. This is a standard practice for transcription given that

the proficiency of a native user differs from that of a second language learner. The native ASL

user was able to transcribe the English to ASL session, however, the ASL transcriber left the

project before the ASL to English session was transcribed. Therefore, the ASL used by Dr. Dudis

was transcribed by the researchers, who are both second language learners of ASL.

When obtaining source language presentations, the goal was to find two presentations

with an comparable level of sophisticated discourse that were given in an academic setting.

While the options for a spoken English presentation were plentiful, the amount of high level

academic lectures that satisfied the time requirements of this study (e.g. over one hour) and were

conducted in ASL were scarce. It is crucial to bear in mind that the “Depiction in ASL” lecture

by Dr. Paul Dudis is a highly specialized topic, and not only concerns but contains linguistic

features that are specific to ASL, have no equivalent in English, and are incredibly difficult to

articulate to an audience not familiar with the language.

Both of the presentations were part of the Department of Interpretation's Lecture Series

and because of this it was likely that the powerpoint presentations could be obtained and used as

preparatory materials for the interpreter. This was true of the English presentation given by Dr.

Hamilton. However, a copy of the powerpoint presentation for Dr. Dudis's lecture was not on file

in the Department of Interpretation. Fortunately, Dr. Dudis was willing to share an overview of

his information regarding depiction. These materials given by Dr. Dudis were not the exact same

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as the powerpoint presentation used in the ASL source language text. This could have had an

impact on the interpretation rendered during the ASL to English session.

Conclusions

There is clearly a relationship between interpreter fatigue and number of omissions when

working from English to ASL for the data gathered in this study. This trend was not paralleled in

the ASL to English data with regard to interpreter omissions, although considering the two

sessions together suggests that there is a plateau phenomenon for lexical omissions. Having

analyzed the data, it also seems apparent that there is a fundamental difference in the interpreting

task between English to ASL and ASL to English. Future studies might benefit from examining

only one of these tasks at a time in order to avoid any differences between the two that might

confound the data (e.g. Prevalence of coding/transliteration in ASL-English work).

There are a multitude of ways this study could be expanded in the future. One approach

would be to compare the work of a novice interpreter as opposed to the work of a seasoned

interpreter. It can be presumed that a seasoned interpreter will manage her fatigue differently

than a novice interpreter as a result of a number of years experience working in a variety of

settings for prolonged periods of time. Another interesting factor to incorporate into this type of

linguistic analysis of interpreter fatigue would be a comparative analysis on an initial interpreted

work and one that was rendered after a full day of interpreting work. Undoubtedly, there are

various factors related with fatigue that will impact interpretation quality. This research will

hopefully spark an interest to further the interpreting field understanding of the effects fatigue

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has on the quality of interpretation. As we strive to provide top quality interpreters, we must have

a clearer understanding of how this can be attained.

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References

Brasel, B. B. (1976). The Effects of Fatigue on Competence of Interpreters for the Deaf, in

Selected Readings in the Integration of Deaf Students at CSUN. (pp. 19-22, Rep.) (H. J.

Murphy, Ed.).

Cokely, D. (1992). Interpretation : A Sociolinguistic Model. New York: Linstok P, Incorporated.

Dean, R. K., & Pollard, R. Q. (2001). Application of Demand-Control Theory to Sign Language

Interpreting: Implications for Stress and Interpreter Training. Journal of Deaf Studies and

Deaf Education, 6(1), 1-14.

Delisle, A., Larivire, C., Imbeau, D., & Durand, M. (2005, June 1). Physical exposure of sign

language interpreters: baseline measures and reliability analysis. European Journal of

Applied Physiology, 944.

Fitzgerald, T. E., & Feuerstein, M. (1992). Biomechanical factors affecting upper extremity

cumulative trauma disorders in sign language interpreters. Journal of Occupational

Medicine, 343.

Gile, D. (1995). Basic concepts and models for interpreter and translator training. Amsterdam: J.

Benjamins Pub. Co.

Johnson, W. L., & Feuerstein, M. (2005). An Interpreter's Interpretation: Sign Language

Interpreters' View of Musculoskeletal Disorders. Journal of Occupational Rehabilitation,

15(3), 401-415.

Madden, M. J. (n.d.). The prevalence of occupational overuse syndrome among Australian sign

language interpreters. Journal of Occupational Health and Safety: Australia and New

Zealand, 11(3), 257-263.

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Napier, J. (2003). A Sociolinguistic Analysis of the Occurance and Types of Omissions Produced

by Australian Sign Language-English Interpreter. In S. Collins, R. Shaw, & M. Metzger

(Eds.), From Topic Boundaries to Omission : New Research on Interpretation. New York:

Gallaudet UP.

Podhorodecki, A. D., & Spielholz, N. I. (1993). Electromyographic study of overuse syndromes

in sign language interpreters. Archives of Physical medicine and Rehabilitation, 74(3),

261-262.

Registry of Interpreters for the Deaf. (1997). Cumulative motion Injury. [Brochure]. Silver

Spring, MD: Author.

Seleskovitch, D. (1994). Interpreting for international conferences problems of language and

communication. Washington, D.C: Pen and Booth.

Stedt, J. D. (1992). Interpreter's Wrist. American Annals of the Deaf, 137(1), 40th ser., 40-43.

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Appendix

Tables- Production Rates

Session 1: Target Language Production Rates (ASL)

Session Number

Clip Number Lexical Items Minutes Signs Per Minute (WPM)

1 1 453 5 90.61 2 445 5 891 3 471 5 94.21 4 437 5 87.4

Session 1: Source Language Production Rates (English)

Session Number

Clip Number Lexical Items Minutes Words Per Minute (WPM)

1 1 801 5 160.21 2 737 5 147.41 3 838 5 167.61 4 797 5 159.4

Session 2: Target Language Production Rates (English)

Session Number

Clip Number Lexical Items Minutes Words Per Minute (WPM)

2 1 645 5 1292 2 564 5 112.82 3 658 5 131.62 4 367 5 73.4

Session 2: Source Language Production Rates (ASL)

Session Number

Clip Number Lexical Items Minutes Signs Per Minute (WPM)

2 1 483 5 96.62 2 411 5 82.22 3 398 5 79.62 4 538 5 107.6

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Tables- Processing Time Analysis

Anchor English (Source)

ASL (Target) Processing Time Time (in

seconds)RobertsCodes of ethicsChallengesLanguageScience of individuals

00:10:49:26 00:10:53:10 3.500:11:15:22 00:11:16:22 1.0

00:11:34:27 00:11:36:06 1.300:13:45:17 00:13:47:22 2.200:13:59:02 00:14:03:09 4.2

Average 2.4


ASL (Target) Processing Time Time

Everyday lifeHepatitis C ProjectBlood pressure readingGallaudet

00:30:05:21 00:30:07:21 2.000:30:52:03 00:30:55:09 2.1

00:32:23:16 00:32:28:26 5.0

00:33:59:11 00:34:02:05 2.5Average 2.9



SubjectiveTake up a stanceMay eleventhObjective

00:50:17:21 00:50:20:13 2.700:50:38:18 00:50:42:28 4.3

00:51:01:15 00:51:04:05 2.700:51:56:18 00:51:59:09 2.7

Session 1 Clip 1 Processing Time Analysis



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“immediate mode”

00:54:46:16 00:54:49:16 3

Average 3.1



EnglishSmokingHIPPASpanishEighteen

01:09:47:15 01:09:49:19 2.101:10:05:29 01:10:07:11 1.401:11:34:23 01:11:39:00 4.201:12:05:21 01:12:09:28 4.201:12:35:07 01:12:37:08 2.0

Average 2.8

Anchor ASL (Source) English (Target) Processing Time Time (in

seconds)IconicDepictingConstructed DialogueCognitive LinguisticsGoldbergSchematic ClauseNew YorkEnglish

0:10:10:03 0:10:13:00 2.90:10:49:15 0:10:50:22 1.20:11:39:25 0:11:42:06 2.4

0:12:13:10 0:12:15:06 1.9

0:12:30:08 0:12:32:08 20:13:20:15 0:13:23:12 2.9

0:13:27:27 0:13:30:29 3.10:14:42:15 0:14:45:09 2.8

Average 2.4

Anchor ASL (Source) English (Target) Processing Time Time

GenericFocusingYesterdaytomorrowsetting

00:30:02:18 00:30:04:12 1.500:30:12:14 00:30:14:23 2.300:30:43:18 00:30:46:06 2.600:32:07:13 00:32:13:06 5.800:33:33:25 00:33:36:09 2.5




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vantage pointstove

00:34:01:03 00:34:03:10 2.200:34:52:11 00:34:55:21 3.3

Average 2.9


Librarynon-manualautomaticfrozen

00:51:13:26 00:51:16:19 2.800:53:23:05 00:53:24:15 1.300:54:32:04 00:54:36:09 4.200:54:46:14 00:54:49:15 3.0

Average 2.8


TreeGrammarVantage Pointreplace soundASL

01:13:30:01 01:13:32:20 2.601:13:52:03 01:13:55:01 2.901:14:13:02 01:14:15:15 2.401:14:51:03 01:14:56:15 5.401:16:47:04 01:16:53:19 6.5

Average 4.0



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