Andaş Çeviride Otomatik Konuşma Tanıma Teknolojisinin İncelenmesi: Sistematik Bir İnceleme

Öz

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.

Anahtar Kelimeler

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

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

Öz

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.

Anahtar Kelimeler

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

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