Andaş Çeviride Otomatik Konuşma Tanıma Teknolojisinin İncelenmesi: Sistematik Bir İnceleme

Abstract

Bu makale, Otomatik Konuşma Tanıma (OKT) teknolojisinin andaş çeviri (AÇ) sürecinde etkinliğini incelemekte olup özellikle tercümanların performansı ve bilişsel yükü üzerindeki etkilerine odaklanmaktadır. Bu çalışma, OKT sistemlerinin sözlü çeviri alanında nasıl kullanıldığını ve bu sistemlerin tercümanların performansına ve bilişsel yüküne olası etkilerini ele almaktadır. Bu incelemede PRISMA 2020 kılavuz ilkelerini benimsenmiştir. Bu doğrultuda EBSCOhost, Web of Science, Scopus ve Google Scholar olmak üzere dört veri tabanlarında, teknoloji odaklı terimler andaş çeviri, performans ve bilişsel yük kavramlarıyla birlikte taranmıştır. Çalışmaya, yalnızca InterpretBank gibi bilgisayar destekli çeviri araçlarına ve Zoom gibi diğer araçlara entegre edilmiş OKT teknolojisinin kullanımına ve OKT destekli simultane çevirinin çevirmenlerin performansına (ör. doğruluk) ve bilişsel yüküne olan etkisine odaklanan, İngilizce dilinde yayınlanmış ve hakemli makaleler dahil edilmiştir. Kriterleri karşılamayan çalışmalar hariç tutulmuştur. Yinelenen çalışmaların çıkarılması ve tarama sürecinin ardından, on beş çalışmanın kriterleri karşıladığı görülmüştür. Makalede incelenen çalışmalar, OKT ile genel performans ve bilişsel yük arasındaki ilişkiyi araştırmak amacıyla, hata analizi (ör. eksiklikler, eklemeler vb.), göz izleme, kulak-ses aralığı (EVS) analizi, görüşmeler ve öznel ölçümler (ör. NASA-TLX ve Paas ölçeği) ile nörofizyolojik teknikler (ör. EEG ve fNIRS) dahil olmak üzere, deneysel tasarımlar ve metodolojiler açısından büyük farklılıklar göstermiştir. Sonuç olarak, elde edilen veriler OKT’nin performansı artırma eğiliminde olduğunu, ancak, tercümanlar üzerinde ek bir bilişsel yük oluşturabileceğini öne sürmektedir. Bununla birlikte, EEG ve fNIRS gibi nörofizyolojik verilere göre ise ASR bilişsel yükü hafifletebilir.

Keywords

• otomatik konuşma tanıma
• andaş çeviri
• bilişsel yük
• çeviri performansı
• PRISMA 2020.

Revealing Automatic Speech Recognition Technology in Simultaneous Interpreting: A Systematic Review

Abstract

This systematic review investigates the effectiveness of Automatic Speech Recognition (ASR) technology in simultaneous interpreting (SI), focusing specifically on its effects on interpreters’ performance and cognitive load. It highlights how ASR systems are employed in the field of interpreting and their potential influences on interpreters’ performance and cognitive load. This review adopted the PRISMA 2020 guidelines. Four databases (EBSCOhost, Web of Science, Scopus and Google Scholar) were searched, combining technology-oriented terms with simultaneous interpreting, performance and cognitive load. Inclusion criteria consisted of peer-reviewed articles in English which exclusively focused on the use of ASR technology both integrated into CAI tools such as InterpretBank and other tools such as Zoom, and the impact of ASR-supported SI on interpreters’ performance (e.g., accuracy) and cognitive load. Studies that did not meet the criteria were excluded. After eliminating duplicates and screening, fifteen studies met the eligibility criteria. The studies varied widely in terms of the experimental designs and methodologies, including error analysis (e.g. omission, additions etc.), eye-tracking, descriptive EVS analysis, interviews and subjective measures (e.g. the NASA-TLX and the Paas scale), and neurophysiological techniques (e.g. EEG and fNIRS) to investigate the relation between ASR and overall performance and cognitive load. Overall, the results suggest that ASR tends to enhance interpreting performance but the assumption is that it may also impose an additional cognitive load on interpreters. Nevertheless, according to neurophysiological data, such as EEG and fNIRS, ASR may alleviate cognitive load.

Keywords

• automatic speech recognition
• simultaneous interpreting
• cognitive load
• interpreting performance
• the PRISMA 2020.

References

1. ATKINSON, Richard C. & SHIFFRIN, Richard M., “The Control of Short-Term Memory”, Scientific American, 225/2, New York 1971, 82-91.
2. BADDELEY, Alan David, HITCH, Graham J.. “Working memory”, The psychology of learning and motivation: Advances in research and theory, (Editor: G. H. Bower), Academic Press, New York, 1974, 47–89.
3. BARIK, Henri C., “A Description of Various Types of Omissions, Additions and Errors of Translation Encountered in Simultaneous Interpretation”, Meta: Journal des traducteurs, 16/4, Montreal 1971, 199.
4. CHEUNG, Andrew K. F., LI, Tianyun, “Machine Aided Interpreting: An Experiment of Automatic Speech Recognition in Simultaneous Interpreting”, Translation Quarterly, 104, Hong Kong 2022, 1–20.
5. COSTA, Hernani, CORPAS PASTOR, Gloria, DURÁN MUÑOZ, Isabel, “Technology-Assisted Interpreting”, Multilingual, 25/3, Sandpoint 2014, 27–32.
6. DEFRANCQ, Bart, FANTINUOLI, Claudio, “Automatic Speech Recognition in the Booth”, Target, 33/1, Amsterdam 2020, 73–102.
7. DESMET, Bart, VANDIERENDONCK, Mieke, DEFRANCQ, Bart, “Simultaneous Interpretation of Numbers and the Impact of Technological Support”, Interpreting and Technology, Language Science Press, Berlin 2018, 13–27.
8. DIRIKER, Ebru, “Simultaneous Interpreting”, Routledge Encyclopedia of Interpreting Studies, (Editor: Franz Pöchhacker), Routledge, London/New York 2015, 382–386.

9. FANTINUOLI, Claudio, “Computer-Assisted Preparation in Conference Interpreting”, Translation & Interpreting, 9/2, Sydney 2017b, 24–37.
10. FANTINUOLI, Claudio, “InterpretBank: Redefining Computer-Assisted Interpreting Tools”, Proceedings of 38th Conference Translating and the Computer, 38, Geneva 2016, 42–52.
11. FANTINUOLI, Claudio, “Interpreting and Technology: The Upcoming Technological Turn”, Interpreting and Technology, (Editor: Claudio Fantinuoli), Language Science Press, Berlin 2018, 1–12.
12. FANTINUOLI, Claudio, “Speech Recognition in the Interpreter Workstation”, Proceedings of the 39th Conference Translating and the Computer, 39, London 2017a, 25–34.
13. FANTINUOLI, Claudio, MARCHESINI, Giulia, LANDAN, Daniel, HORAK, Lukas, “Kudo Interpreter Assist: Automated Real-Time Support for Remote Interpretation”, https://arxiv.org/abs/2201.01800, (Accessed: 5 January 2026).
14. FANTINUOLI, Claudio, MONTECCHIO, Marco, “Defining Maximum Acceptable Latency of AI-Enhanced CAI Tools”, https://arxiv.org/abs/2201.02792, (Accessed: 5 January 2026).
15. GERVER, David, “A Psychological Approach to Simultaneous Interpretation”, Meta, 20/2, Montreal 1975, 119-128.
16. GILE, Daniel, Basic Concepts and Models for Interpreter and Translator Training, John Benjamins Publishing Company, Amsterdam/Philadelphia 2009.
17. GUO, Meng, XIE, Yuxing, HAN, Lili, LEI,Victoria Lai Cheng, LI, Defeng, “From Sight to Insight: Unravelling Student Interpreters’ Usage and Perceptions of Live Captioning in Chinese–English Simultaneous Interpreting”, Linguistica Antverpiensia, New Series: Themes in Translation Studies, 24, Antwerp 2025,160–187.
18. HARRIS, Joshua D., QUATMAN, Carmen E., MANRING, M. M., SISTON, Robert A., FLANIGAN, David C., “How to Write a Systematic Review”, The American Journal of Sports Medicine, 42/11, USA 2014, 2761–2768.
19. JIANG, Yuhe, “Human–Machine Interaction in Simultaneous Interpreting Workflow: The Effect of ASR-Generated Live Captions on Student Interpreting of Speeches at Different Rates”, Interpreting and Society: An Interdisciplinary Journal, 5/2, Beijing 2025, 1–18.
20. JURAFSKY, Daniel & MARTIN, James, “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition”, Upper Saddle River: Pearson Prentice Hall, 2009.
21. LI, Tianyun, CHIMEL, Agnieszka, “Automatic Subtitles Increase Accuracy and Decrease Cognitive Load in Simultaneous Interpreting”, Interpreting: International Journal of Research and Practice in Interpreting, 26/2, Philadelphia 2024, 253–281.
22. MESHKATI, Najmedin, “Toward Development of a Cohesive Model of Workload”, Human Mental Workload, (Editors: Peter A. Hancock, Najmedin Meshkati), Elsevier Science Publishers B.V. (North-Holland), Amsterdam 1988, 305–320.
23. MILLER, George A., “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information”, Psychological Review, 63/2, USA 1956, 81–97.
24. MIRZAEE, Adeleh, RAZAVI, Mir Saeed Mousavi, PARHAM, Fatemeh, DADGOSTAR, Mehrdad, “The Effect of Automatic Speech Recognition on Iranian Interpreters’ Cognitive Load: An fNIRS Study”, SKASE Journal of Translation and Interpretation, 18/2, Slovakia 2025, 240–258.
25. PAAS, Fred G., VAN MERRIËNBOER, Jeroen J., “Instructional Control of Cognitive Load in the Training of Complex Cognitive Tasks”, Educational Psychology Review, 6/4, USA 1994, 351–371.
26. PAGE, Matthew J., Joanne E. MCKENZIE, Patrick M. BOSSUYT, Isabelle BOUTRON, Tammy C. HOFFMANN, Cynthia D. MULROW, Larissa SHAMSEER, Jennifer M. TETZLAFF, Elie A. AKL, Sue E. BRENNAN, Roger CHOU, Julie GLANVILLE, Jeremy M. GRIMSHAW, Asbjørn HRÓBJARTSSON, Manoj M. LALU, Tianjing LI, Elizabeth W. LODER, Evan MAYO-WILSON, Steve MCDONALD, Luke A. MCGUINNESS, Lesley A. STEWART, James THOMAS, Andrea C. TRICCO, Vivian A. WELCH, Penny WHITING & David MOHER, “The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews”, BMJ, 372, London 2021, n71.
27. POCHHACKER, Franz, Introducing Interpreting Studies, Routledge, New York 2016.
28. PRANDI, Bianca, “Designing a Multimethod Study on the Use of CAI Tools During Simultaneous Interpreting”, Proceedings of the 39th Conference Translating and the Computer, Cenevre 2017, 76-88.
29. RODRÍGUEZ, Susana, GRETTER, Roberto, MATASSONI, Marco, FALAVIGNA, Daniele, ALONSO, Álvaro, CORCHO, Oscar, RICO, Mariano, “SmarTerp: A CAI System to Support Simultaneous Interpreters in Real-Time”, Proceedings of TRITON 2021, 2021, 102–109.
30. RUSSELLO, Chiara, CARBUTTO, Marta, “Enhancing Numerical Accuracy in Simultaneous Interpreting Students: An Exploratory Study Comparing Human and AI-Based Support”, The Interpreters’ Newsletter, 28, Trieste 2023, 69–90.
31. SEEBER, Kilian G., “Cognitive Load in Simultaneous Interpreting: Measures and Methods”, Target: International Journal of Translation Studies, 25/1, Amsterdam 2013, 18–32.
32. SEEBER, Kilian G., “Cognitive Load in Simultaneous Interpreting: Existing Theories — New Models”, Interpreting, 13/2, Philadelphia 2011, 176–204.
33. STRECH, Daniel, SOFAER, Neema, “How to Write a Systematic Review of Reasons”, Journal of Medical Ethics, 38/2, UK 2012, 121–126.
34. SU,Wenchao, LI, Defeng, “Eye-Tracking Visual Attention of Professional Interpreters during Technology-Assisted Simultaneous Interpreting”, Linguistica Antverpiensia, New Series: Themes in Translation Studies, 24, Antwerp 2025, 188–212.
35. SWARTZ, Marie K., “PRISMA 2020: An Update”, Journal of Pediatric Health Care, 35/4, Philadelphia 2021, 351.
36. TAMMASRISAWAT, Pannapat, RANGPONSUMRIT, Nunghatai, “The Use of ASR-CAI Tools and Their Impact on Interpreters’ Performance during Simultaneous Interpretation”, New Voices in Translation Studies, 28/2, Bangkok 2023, 25–51.
37. ÜNLÜ, Cihan, Automatic Speech Recognition in Consecutive Interpreter Workstation: Computer-Aided Interpreting Tool “Sight-Terp”, Hacettepe University, Department of Translation and Interpretation, Ankara 2023.
38. WANG, Xiaoman, WANG, Binhua, YUAN, Lu, “The Function of ASR-Generated Live Transcription in Simultaneous Interpreting: Trainee Interpreters’ Perceptions from Post-Task Interviews”, Humanities and Social Sciences Communications, 12, London 2025, 1-12.
39. XU, Lang, “Exploring Cognitive Processing in Simultaneous Interpreting with Live Captioning: A Pilot Study on Ear-Voice Span and Output Accuracy”, Journal of Translation Studies, 5/1, Bristol 2025, 77–106.
40. YUAN, Lu, WANG, Binhua, “Cognitive Processing of the Extra Visual Layer of Live Captioning in Simultaneous Interpreting: Triangulation of Eye-Tracked Process and Performance Data”, Ampersand, 11, Oxford 2023, 1-9.
41. ZHANG, Wenkang, XIE, Rui, “Monolingual versus Bilingual Captioning: An Ergonomic Perspective on Computer-Assisted Simultaneous Interpreting”, Interpreting and Society: An Interdisciplinary Journal, 5/2, Beijing 2025, 131–153.