Development of Web-based Teaching Material for
Simultaneous Interpreting Learners using Bilingual Speech Corpus
Hitomi Tohyama
Graduate School of Information Science, Nagoya University, Japan
hitomi@el.itc.nagoya-u.ac.jp
Shigeki Matsubara
Information Technology Center, Nagoya University, Japanmatubara@itc.nagoya-u.ac.jp

Abstract: CIAIR of Nagoya University, a simultaneous interpretation corpus between Japanese andEnglish has been constructed over five years. The size of transcribed data is about 1 million words andthis corpus therefore would deserve to be called the simultaneous interpretation resources of thelargest-in-the-world class. This paper suggests a method of extracting the data for training future
simultaneous interpreters from the corpus according to difficulty order. This method was developed
based on the simultaneous interpretation theory having been constructed upon the analysis of thecorpus. This paper also describes two kinds of tools for supporting learners to acquire the complex
skills of simultaneous interpretation. These tools made it possible to help them by providing the new
way of learning the interpretation skills, which is to show the professional interpreters’ techniques asvisible examples, instead of the traditional way of training interpreters in college, just to interprettape-recorded lectures during the lesson.

1. Introduction
In recent years, many universities and educational institutions have been providing the curriculum for developing
practical human resources that especially focuses on training undergraduates and graduate students who havespecialized potential (Rikkyo Univ. 2005, Nagoya Univ. 2005). Courses on interpretation are one of thosecurriculums and quite a few universities open the courses on interpretation theory and the seminars for acquiring
interpretation skill. In this academic climate, there has been growing interest in the study about interpreter education(Korrnakov 2000).
Today, many materials for learning English have been produced and some of them are specialized for e-learning.
However, there have been just a few effective materials and training courses developed for the students who alreadyhave high English proficiency and who can speak English fluently. Little effort has been made for developing support
materials that can be used for teaching simultaneous interpretation skill. The reasons of this low level of attention todeveloping materials for simultaneous interpretation skill are (1) that effective training theory is yet to be establishedbecause researchers still don’t understand the whole aspect of simultaneous interpretation, the human brain activity
which requires the maximum level of human working memory and (2) that there exists no enormous data of
English-Japanese simultaneous interpretation. Therefore, the construction of simultaneous interpretation theory basedon enormous data and the development of simultaneous interpreter training materials by analyzing the data has long
been awaited.
This paper suggests a method of extracting the data for training future simultaneous interpreters from a bilingual
corpus (Kawagchi, et al. 2003). This method was developed based on the simultaneous interpretation theory having
been constructed upon the analysis of the corpus. This paper also describes two kinds of support tools for learners.
These tools support learners to acquire difficult skills such as “controlling utterance speed,” “interpreting timing,”“controlling the length of pause,” “dividing the whole speech into interpretation units,” and “utilizing theinterpretation patterns.” These tools made it possible to help them by providing the new way of learning theinterpretation skills, which is to show the professional interpreters’ techniques as visible examples, instead of thetraditional way of training interpreters in college, just to interpret tape-recorded lectures during the lesson.
In this paper, we describe the design of the CIAIR (Center for Integrated Acoustic Information Research)
Simultaneous Interpretation Corpus in Secton2. In Section 3, we propose the way to develop training materials byusing the corpus while referring the simultaneous interpretation theory constructed from data analysis. In Section 4,
we describe tools for training simultaneous interpretation skill, which have been under development with the teachers
and instructors engaging simultaneous interpretation training. In Section 5, we consider the significance ofdeveloping support tools for training interpreters by using simultaneous interpretation corpus and also discuss thefurther issues.

2. CIAIR Simultaneous Interpretation Corpus
A simultaneous interpretation corpus (between Japanese and English) has been constructed by CIAIR (Tohyama, et al.
2004). The CIAIR has aimed at the realization of the multi-lingual communication supporting environment andconstruction of a simultaneous interpreting theory (Tohyama, et al. 2006[6,7,8]). The corpus recorded bothmonologue and dialogue data. The contents of the corpus are daily topics (economics, history, and, culture, etc.). Thecorpus targets English and Japanese. The recording time is 182 hours in total. The speech data has been alltranscribed and visualized (as Figure 1 shows). The size of transcribed data is about 1 million words, and the corpus
would deserve to be called the simultaneous interpretation corpus of the largest-in-the-world class. This corpus
consists of three parts, the speech data, the text data, and environment data. Moreover, a bilingual/parallel corpus ofsimultaneous interpretation has also been constructed as a part of this project. The discourse tag and the utterance
time tag were given to the corpus. In this paper, we discuss the teaching material development that uses
monologue data. Moreover, each monologue speech is interpreted by two or four professional interpreters.
Therefore, it becomes possible to compare those interpretation examples in a sample of utterance. The speech wasrecorded for about 10 minutes per lecture.
CIAIR
Simultaneous Interpretation Corpus

Monologue

Environment

Dialogue
J－E

E－JSpeech

Text

English speaker’s files

SpeechEnvironment

Text

English speaker’s files
Japanese speaker’s files

E - J interpreter’s files
E - J interpreter’s files
J - E interpreter’s files

Figure 1:3.

Structure of the CIAIR simultaneous interpretation corpus

Construction of Interpretation Theory and Classification of Data for Teaching Material
from the Corpus

In many universities, the courses on interpretation are usually opened for such students who have high English
competence and have TOEIC score 850 or more. Thus, the purpose of those courses is not to learn English but toacquire interpretation skill. Simultaneous interpretation skill is particularly difficult and complicated compared withconsecutive interpretation because interpreters have to make his/her utterance while they are processing the incoming
sentences instead of waiting for the end of sentences. In order to develop support tools for the simultaneousinterpreters, it is necessary to focus on the following three points:

1.

2.
3.

3.1

To collect the simultaneous interpreters’ strategies, that is, how they follow the incoming sentences ofspeakers and make their utterance without delay, by analyzing enormous data of the utterance ofprofessional simultaneous interpreters quantitatively and qualitatively
To extract the interpretation data according to the difficulty level of interpretation skill from the corpus
built upon the simultaneous interpretation theory established so far
To develop the support tools which enable learners to acquire interpretation skill efficiently incollaboration with professional simultaneous interpreters and the educators of interpretation

Analysis of Simultaneous Interpreter's Strategies

This section briefly explains some points about the interpretation theory which has been constructed from theanalysis of corpus. Strategies a), b), c) are the measures index that are used when the data for developing teachingmaterials is extracted in descending order of difficulty from the large-scale simultaneous interpretation corpus
mentioned in Section 3.2. Strategy d) is referred in section 4.
Strategy a) Controlling utterance speed
According to the strategy proposed by Tohyama et al, the utterance speed of simultaneous interpreters has been
observed carefully in terms of their strategy for following speaker utterances (Tohyama, et al 2005[6]). The
result of analysis shows that there are two patterns in their interpretation, A Pattern and B Pattern. We can see
one pattern of the strategies used by the simultaneous interpreters in Pattern A in which they control theirutterance speed along with speakers’ utterance speed. In Pattern B, we can see another strategy, in which theykeep their utterance speed steady however fast or slow speakers speak. (For learners, B Pattern, which shows
how the interpreters maintain their own utterance speed, is preferable.)
Strategy b) Controlling delay time
Tohyama et al. paid attention to simultaneous interpreters’ distribution of interpretation delay time, rollback operation forthe delay time and interpretation start timing so as to observe their following-the-original-language strategy. As a result, they
identified one following pattern which the interpreter takes long delay time and various interpreting start timing and thus
there are a number of delay interpretation units. The other following pattern identified is that the interpreter doesn’t takealmost no delay time and delay interpretation units and her interpreting timing is relatively constant.
Strategy c) Controlling the length of pause
To control the length of pause is closely related to the interpretation strategy and pause influences the process in which
listeners understand what they hear (Tohyama, et al 2005[7]). They conducted experiments to clarify this influence byusing 31 subjects and two different types of simultaneous interpretation data, these being free-utterance lectures without aprepared script (A-style lectures), and lectures based on prepared scripts (B-style lectures). They selected 12 A-style lectures
and 9 B-style lectures from the CIAIR Simultaneous Interpretation Corpus. The results reveal that in A-style lectures wherethe speed of speeches was relatively low, it was ascertained that the lengths of pauses appearing in interpreters’ utteranceswere short in cases which the subjects evaluated as listener-friendly interpretation. In B-style lectures where the speed ofspeeches was high, it was ascertained that the length of interpreters’ pauses has little influence on the subjects’ listener
impressions. Moreover, they found a common feature in both lecture styles: the listener impressions were based on thestability of the speech-pause period and the presence of rhythm.
Strategy d) Making good use of site translation pattern
The simultaneous interpreters are required to generate target speeches simultaneously with source speeches. Therefore, they
have various kinds of strategies to raise simultaneity. In this investigation, the simultaneous interpreting patterns with highfrequency and high flexibility were extracted from the corpus (Tohyama, et al. 2006[8]).

3.2 Extract the Interpreter Data According to the Difficulty
Figure 2 shows the process of extracting the interpretation data appropriate for teaching materials by the difficultylevel of interpretation skill from the corpus built upon the simultaneous interpretation theory established so far. The
procedure is shown as follows:*Step 1Categorize the lecturers’ speech data into two groups, the data with high utterance speed and the data with lowutterance speed.
*Step 2After the calculation of coefficient of variation has done, categorize those data into two groups, the group withmore utterance speed variations and the group with fewer utterance speed variations.

*Step 3
Calculate the coefficient of variation of utterance speed. Categorize those data into two groups, one group in which
speakers use the strategy that they adjust their utterance speed to that of lecturers and the other in which speakers
employ the strategy that they maintain their own speed.
*Step 4Using the parallel corpus in the CIAIR corpus, calculate the time the interpreters lost while they are processing theincoming sentences in each aligned unit. (To shorten the delay time is more difficult)

CIAIR SI Corpus*Step 1 lecturer’s utterance speed calculation
low speedhigh speed*Step 2 lecturer’s speed cv calculation
cv-low

cv-highspeed cv calculation

speed cv calculation

cv-high*Step 3 speed cv calculation
cv-low*Step 4 delay time average
short

long

lecturer’s speed cv calculation

cv-highcv-lowdelay time average

delay time average

short

short

long

cv-low

cv-highcv-lowspeed cv calculation

cv-highcv-low

cv-highdelay time averagedelay time average
delay time average

long

short

short

long

short

long

long

pause time averagepause time averagepause time average *Step 5
short

long

short

long

short

long

pause time cv pause time cv pause time cv pause time cv pause time cv pause time cv pause time cv pause time cv pause time cv *Step 6cv-low cv-high cv-low cv-high cv-low cv-high cv-low cv-high cv-low cv-high cv-low cv-high cv-low cv-high cv-low cv-high cv-low cv-highLevel9Level8Figure 2:

Level7

Level6

Level5

Level4

Level3

Level2

Level1

Process of extracting the various interpreter data according to the difficulty* Parallelograms show the processing of data. * All boxes show the text data and the speech data.
*The utterance speed and the coefficient of variation can be calculated from the numbers ofmora per utterance unit and time information.
*cv: coefficient of variation*Step 5
Calculate the average time of pauses in one interpretation case by using the interpreters’ data. The interpretation
with shorter average pause time is better, although it was clarified from our experiment that whether pause timeshort or long does not affect the listeners’ impression when lecturers speak fast. Thus, we did not take into
consideration the pauses in the cases with faster lecturers’ utterance speed.
*Step 6Using the interpreters’ data, investigate the variation of pause length in interpreters’ utterance. The cases with thesame length of pause in each utterance unit should be considered as better interpretation generated by advanced
interpretation skills.

4. Support Tool for Acquiring Interpretation Skills
In section 3, we described the method for extracting of interpretation data (text and speech data) for teachingmaterials from the corpus that vary in the levels of difficulty. For example, the data in which speakers speak with fastand unstable speed while interpreters keep own speed, which means interpreter’s delay time and pauses length arestable. The level 1 data which is the date with slow and stable utterance speed of lecture would be preferable for thebeginners.
Next issues are how to provide those interpretation data to the learners and how to make the process of theiracquiring interpretation skills easier. We have developed two kinds of support tools for future interpreters. Becausethese tools enable learners to perceive the interpretation skill by using their visual and auditory sense, their learning
process will be facilitated.
They have been developed as software which can be performed on the Web server, and a user can refer to the data
easily by using a browser. There are 4 cases of interpretation per one lecture in the simultaneous interpretation corpus.
Thus, it is possible to compare the interpretation techniques or skills seen in one interpretation case to those of theother cases.
File selection box

Lecturer’s utterance time

Interpreter’s utterance time

Discourse tag(x); Reduced

Elapsed time(s)Discourse tag(F); Filled pauses

ScrollWindow balancing

Figure 3:

Sample of time chart tool

4.1 Time Chart Tool
We visualized the simultaneous interpreters’ utterance or, in other words, the way they follow speakers’ utterance, inchronological order in Figure 3. The numbers on the left column shows how long it has passed from the beginning ofthe lecture. The column next to the time column shows what the lecturers and interpreters said. The learners canstudy the utterance timing, the length of pause and how much the interpreter’s utterance overlaps that of lecturer (seesection 3.2, strategy a) - c)). They can also feel the speed of the interpreter’s utterance because they can listen to what
had actually been said as they use this tool.
4.2 Parallel Interpretation Tool
In Figure 4, we showed the learning-support tool of interpretation units developed from the parallel/bilingual corpus.
Learners are able to learn how the interpreters divide the whole speech of the lecturer into interpretation units. And
because utterance tags being attached, they can also observe how the interpreter corrects his or her misinterpretation
while they are interpreting (Please refer to 3.2, strategy d)).

5. Conclusion
In this paper, the development of support tool for future interpreters using the CIAIR simultaneous interpretation
corpus, the complicated strategies of professional simultaneous interpreters have been clarified. This enormous
corpus also has been categorized into appropriate order of difficulty by using interpreters’ strategies as index. To
facilitate learners’ acquiring the interpretation skills, two kinds of support tools was developed from the visible andFile link
Lecturer’s utterance

File name
Parallel

Aligned ID

Interpreter’s utterance

ScrollUtterance ID

Utterance start-end

Sentence bleak tag

Figure 4:Sample of parallel interpretation tool

audible data of the CIAIR simultaneous interpretation corpus. As a consequence, these tools made it possible andeasy to support their learning process because these tools show the learners the processional interpreters’ ways ofinterpretation as examples while the traditional way of training future interpreters had been just to let them listen totape-recorded lectures. The opinion on these assisting tools from professional simultaneous interpreters, theinstructors of interpretation and the learners/future interpreters is being collected and these tools have gotten fairly
high evaluation from them. Now we are working on the improvement of the support tools with those who areinvolved in interpreter education. We are planning to develop those tools with feedback function for learners in near
the future. The CIAIR corpus has been already exhibited. For more details, please refer to the following URL:
http://slp.el.itc.nagoya-u.ac.jp/sidb/
Acknowledgments
The authors would like to thank Prof. Toyohide Watanabe, Assoc. Prof. Nobuo Kawaguchi at the Nagoya University,
Mr. Aizawa and Mr. Takagi of Matsubara Laboratory alumni for their precious advice. This work is partially
supported by the Grand-in-Aid for Exploratory Research (No. 17652040) of JSPS.

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