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Translation and Interpreting Studies: Online-First Articles

The effectiveness of computer-assisted interpreting

A preliminary study based on English-Chinese consecutive interpreting

ORCID logo | Southwest University
ORCID logo | Macquarie University | North-West University

Facing a new technological turn, the field of interpreting is in great need of evidence on the effectiveness of computer-assisted interpreting. This study proposes a computer-assisted consecutive interpreting (CACI) mode incorporating speech recognition (SR) and machine translation (MT). First, the interpreter listens to the source speech and respeaks it into an SR system, creating an SR text which is then processed by an MT system. Second, the interpreter produces a target speech with reference to the SR and MT texts. Six students participated in training on CACI, after which they performed consecutive interpreting in both the conventional and the new mode. The study finds that CACI featured fewer pauses and reduced cognitive load. Moreover, the overall interpreting quality, especially the accuracy, was increased. The effectiveness of the new mode is found to be modulated by the interpreting direction.

Keywords: computer-assisted interpreting, consecutive interpreting, cognitive load, speech recognition, machine translation
Article outline
  • Introduction
  • Interpreting and technology
    • Setting-oriented and process-oriented interpreting technologies
    • Bridging process-oriented technologies for use in interpreting
    • The application of SR and MT to interpreting
    • A CACI mode
    • Research questions
  • Method
    • Participants
    • Apparatus
    • Training
    • Stimulus
    • Procedure
    • Data and analysis
  • Results
    • Differences in the interpreting process
      • A comparison of the fluency
      • A comparison of the cognitive load
    • Differences in the interpreting product
      • Overall quality measured by scale rating
      • Accuracy measured by propositional rating
  • Discussion
  • Conclusion
  • Acknowledgements
  • Notes
  • References
Published online: 5 December 2022
DOI logo
https://doi.org/10.1075/tis.21036.che
References
AIIC
2002 “Interpreter Workload Study – Full Report.” [URL]. Last accessed 21 June 2022.
Arivazhagan, Naveen, Colin Cherry, Te, I., Wolfgang Macherey, Pallavi Baljekar, and George Foster
2020 “Re-translation strategies for long form, simultaneous, spoken language translation.” In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings, Barcelona, May 4–8, 2020 1, 7919–7923. Piscataway: IEEE. DOI logoGoogle Scholar
Azarmina, Pejman, and Paul Wallace
2005 “Remote interpretation in medical encounters: A systematic review.” Journal of Telemedicine and Telecare 111: 140–145. DOI logoGoogle Scholar
Baddeley, Alan
2012 “Working memory: Theories, models, and controversies.” Annual Review of Psychology 631: 1–29. DOI logoGoogle Scholar
Chen, Sijia
2017 “The construct of cognitive load in interpreting and its measurement.” Perspectives 25(4): 640–657. DOI logoGoogle Scholar
2020 “The impact of directionality on the process and product in consecutive interpreting between Chinese and English: Evidence from pen recording and eye tracking.” JoSTrans: The Journal of Specialised Translation 341: 100–117.Google Scholar
Chmiel, Agnieszka
2016 “Directionality and context effects in word translation tasks performed by conference interpreters.” Poznań Studies in Contemporary Linguistics 52(2): 269–295. DOI logoGoogle Scholar
Costa, Hernani, Gloria Corpas Pastor, and Isabel Durán Muñoz
2018 “Assessing terminology management systems for interpreters.” In Trends in E-Tools and Resources for Translators and Interpreters, ed. by Gloria Corpas Pastor and Isabel Durán-Muñoz, 57–84. Leiden: Brill. DOI logoGoogle Scholar
Cunningham, Hetty, Linda F. Cushman, Cecilia Akuete-Penn, and Dodi D. Meyer
2008 “Satisfaction with telephonic interpreters in pediatric care.” Journal of The National Medical Association 100(4): 429–434. DOI logoGoogle Scholar
Davitti, Elena, and Annalisa Sandrelli
2020 “Embracing the complexity: A pilot study on interlingual respeaking.” Journal of Audiovisual Translation 3(2): 103–139. DOI logoGoogle Scholar
Dawson, Hayley
2019 “Feasibility, quality and assessment of interlingual live subtitling: A pilot study.” Journal of Audiovisual Translation 2(2): 36–56. DOI logoGoogle Scholar
Dawson, Hayley, and Pablo Romero-Fresco
2021 “Towards research-informed training in interlingual respeaking: An empirical approach.” The Interpreter and Translator Trainer 15(1): 66–84. DOI logoGoogle Scholar
de Jong, Nivja H., Rachel Groenhout, Rob Schoonen, and Jan H. Hulstijn
2015 “Second language fluency: Speaking style or proficiency? Correcting measures of second language fluency for first language behavior.” Applied Psycholinguistics 36(2): 223–243. DOI logoGoogle Scholar
Desmet, Bart, Mieke Vandierendonck, and Bart Defrancq
2018 “Simultaneous interpretation of numbers and the impact of technological support.” In Interpreting and Technology, ed. by Claudio Fantinuoli, 13–27. Berlin: Language Science Press.Google Scholar
Devaux, Jérôme
2016 “When the role of the court interpreter intersects and interacts with new technologies.” In Intersect, Innovate, Interact. Ctis Occasional Papers, 7 1, ed. by Pauline Henry-Tierney and Dinithi Karunanayake, 4–21. Manchester: Centre for Translation and Intercultural Studies.Google Scholar
2018 “Technologies and role-space: How videoconference interpreting affects the court interpreter’s perception of her role.” In Interpreting and Technology, ed. by Claudio Fantinuoli, 91–117. Berlin: Language Science Press.Google Scholar
Fantinuoli, Claudio
2018a “Computer-assisted interpreting: Challenges and future perspectives.” In Trends in E-tools and Resources for Translators and Interpreters, edited by Isabel Durán-Muñoz and Gloria Corpas Pastor, 153–174. Leiden: Brill. DOI logoGoogle Scholar
2018b “Interpreting and technology: The upcoming technological turn.” In Interpreting and Technology, ed. by Claudio Fantinuoli, 1–12. Berlin: Language Science Press.Google Scholar
Fantinuoli, Claudio, and Bianca Prandi
2021 “Towards the evaluation of automatic simultaneous speech translation from a communicative perspective.” In Proceedings of the 18th International Conference on Spoken Language Translation (Iwslt 2021), 245–254. Bangkok: Association for Computational Linguistics. DOI logoGoogle Scholar
Goldsmith, Joshua
2018 “Tablet interpreting: Consecutive interpreting 2.0.” Translation and Interpreting Studies 13(3): 342–365. DOI logoGoogle Scholar
Hamidi, Miriam, and Franz Pöchhacker
2007 “Simultaneous consecutive interpreting: A new technique put to the test.” Meta 52(2): 276–289. DOI logoGoogle Scholar
Hart, Sandra G., and Lowell E. Staveland
1988 “Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research.” In Human Mental Workload, ed. by Peter A. Hancock and Najmedin Meshkati, 139–183. Amsterdam: Elsevier. DOI logoGoogle Scholar
Karimova, Sariya, Patrick Simianer, and Stefan Riezler
2018 “A user-study on online adaptation of neural machine translation to human post-edits.” Machine Translation 321: 309–324. DOI logoGoogle Scholar
Li, Ping, Fan Zhang, Anya Yu, and Xiaowei Zhao
2020 “Language History Questionnaire (LHQ3): An enhanced tool for assessing multilingual experience.” Bilingualism: Language and Cognition 23(5): 938–944. DOI logoGoogle Scholar
Licoppe, Christian, Maud Verdier, and Claire-Antoine Veyrier
2018 “Voice, power, and turn-taking in multilingual, consecutively interpreted courtroom proceedings with video links.” In Here or There: Research on Interpreting Via Video Link, ed. by Jemina Napier, Robert Skinner and Sabine Braun, 299–322. Washington, DC: Gallaudet University Press. DOI logoGoogle Scholar
Lin, Yumeng, Qianxi Lv, and Junying Liang
2018 “Predicting fluency with language proficiency, working memory, and directionality in simultaneous interpreting.” Frontiers in Psychology 91: 1–13. DOI logoGoogle Scholar
Liu, Qun, and Xiaojun Zhang
2015 “Machine translation: General.” In Routledge Encyclopedia of Translation Technology, ed. by Sin-wai Chan, 105–119. London: Routledge.Google Scholar
Mellinger, Christopher D., and Thomas A. Hanson
2018 “Interpreter traits and the relationship with technology and visibility.” Translation and Interpreting Studies 13(3): 366–392. DOI logoGoogle Scholar
Najafian, Maryam, and Martin Russell
2020 “Automatic accent identification as an analytical tool for accent robust automatic speech recognition.” Speech Communication 1221: 44–55. DOI logoGoogle Scholar
Nicodemus, Brenda, and Karen Emmorey
2015 “Directionality in ASL-English interpreting: Accuracy and articulation quality in L1 and L2.” Interpreting 17(2): 145–166. DOI logoGoogle Scholar
Orlando, Marc
2014 “A study on the amenability of digital pen technology in a hybrid mode of interpreting: Consec-simul with notes.” Translation & Interpreting 6(2): 39–54.Google Scholar
Orlando, Marc, and Jim Hlavac
2020 “Simultaneous-consecutive in interpreter training and interpreting practice: Use and perceptions of a hybrid mode.” The Interpreters’ Newsletter 251: 1–17.Google Scholar
Peris, Álvaro, Miguel Domingo, and Francisco Casacuberta
2017 “Interactive neural machine translation.” Computer Speech & Language 451: 201–220. DOI logoGoogle Scholar
Pisani, Elisabetta, and Claudio Fantinuoli
2021 “Measuring the impact of automatic speech recognition on number rendition in simultaneous interpreting.” In Empirical Studies of Translation and Interpreting, ed. by Caiwen Wang and Binghan Zheng, 181–197. New York: Routledge. DOI logoGoogle Scholar
Pöchhacker, Franz
2016 Introducing Interpreting Studies (Second Edition). London: Routledge. DOI logoGoogle Scholar
Pöchhacker, Franz, and Aline Remael
2019 “New efforts? A competence-oriented task analysis of interlingual live subtitling.” Linguistica Antverpiensia 181: 130–143. DOI logoGoogle Scholar
Quené, Hugo, Ingrid Persoon, and Nivja de Jong
2010 “Praat script syllable nuclei.”Google Scholar
Rinne, Juha O., et al
2000 “The translating brain: Cerebral activation patterns during simultaneous interpreting.” Neuroscience Letters 294(2): 85–88. DOI logoGoogle Scholar
Romero-Fresco, Pablo
2011 Subtitling through Speech Recognition: Respeaking. Manchester: Routledge.Google Scholar
2012 “Respeaking in translator training curricula: Present and future prospects.” The Interpreter and Translator Trainer 6(1): 91–112. DOI logoGoogle Scholar
Romero-Fresco, Pablo, and Carlo Eugeni
2020 “Live subtitling through respeaking.” In The Palgrave Handbook of Audiovisual Translation and Media Accessibility, ed. by Łukasz Bogucki and Mikołaj Deckert, 269–295. Cham: Palgrave. DOI logoGoogle Scholar
Roziner, Ilan, and Miriam Shlesinger
2010 “Much ado about something remote: Stress and performance in remote interpreting.” Interpreting 12(2): 214–247. DOI logoGoogle Scholar
Seeber, Kilian G.
2011 “Cognitive load in simultaneous interpreting: Existing theories – New models.” Interpreting 13(2): 176–204. DOI logoGoogle Scholar
Tommola, Jorma, and Marketta Helevä
1998 “Language direction and source text complexity: Effects on trainee performance in simultaneous interpreting.” In Unity in Diversity? Current Trends in Translation Studies, ed. by Lynne Bowker, Michael Cronin, Dorothy Kenny and Jennifer Pearson, 177–186. Manchester: St. Jerome.Google Scholar
Vieira, Lucas Nunes
2019 “Post-editing of machine translation.” In The Routledge Handbook of Translation and Technology, ed. by Minako O’Hagan, 319–335. London: Routledge. DOI logoGoogle Scholar
Wang, Xinyu, and Caiwen Wang
2019 “Can computer-assisted interpreting tools assist interpreting?” Transletters. International Journal of Translation and Interpreting 31: 109–139.Google Scholar
Xiong, Hao, Ruiqing Zhang, Chuanqiang Zhang, Zhongjun He, Hua Wu, and Haifeng Wang
2019 Dutongchuan: Context-Aware Translation Model for Simultaneous Interpreting. DOI logoGoogle Scholar
Yu, Dong, and Li Deng
2015 Automatic Speech Recognition: A Deep Learning Approach. London: Springer.Google Scholar