Küresel Sesler, Yerel Kayıplar: YouTube Kanalı “Vlad”da Yapay Zekâ Tarafından Üretilen Dublajın Kalite Değerlendirmesi

Öz

YouTube’da yapay zekâ tarafından üretilen dublaj, hızlı ve ölçeklenebilir çok dilli içerik dağıtımını mümkün kılmıştır; ancak kültürel manipülasyon, ideolojik yeniden çerçeveleme ve anlatı kontrolü bağlamında ciddi endişeler doğurmaktadır. Bu çalışma, üslup, pragmatik, mizah ve kültürel referanslardaki değişikliklere odaklanarak, YouTube’da yapay zekâ tarafından üretilen dublajın anlamı nasıl şekillendirdiğini incelemektedir. YZ tarafından otomatik olarak üretilen dublaj, genellikle kültürel bütünlükten ziyade sadece dudak senkronizasyonunun doğruluğuna ve izleyicilere çoklu dil seçenekleri sunmaya öncelik verir; bu da, daha fazla takipçiye ulaşmak ve onları kanala abone olmaya teşvik etmek gibi ticari hedefleri desteklerken anlam aktarımında hatalara yol açar. Bu gerçeğe dayanılarak, mevcut çalışma, çocuk izleyicileri hedef alan “Vlad” isimli kanaldan seçilen Türkçe dublaj çevirilerinin amaçlı örneklemesinden elde edilen verilerin nitel bir incelemesini yaparak, YZ tarafından üretilen dublajın kültürel temsili nasıl etkilediğini ortaya koymayı amaçlamaktadır. Çalışmadan elde edilen veriler, Larson’ın Anlam Temelli Çeviri Kalitesi Değerlendirme (TQA) modeline göre tekrarlanan kalıplara göre sınıflandırılmıştır. Bir başka deyişle, kasıtlı olarak seçilen örnekler beş hata türü çerçevesinde sınıflandırılmıştır: (1) anlamsal hatalar, (2) pragmatik uyumsuzluklar, (3) eksiklikler ve eklemeler, (4) görsel-işitsel uyumsuzluk ve (5) konuşma senteziyle ilgili sorunlar; bunlar, doğruluk, doğallık ve netlik dahil olmak üzere Larson’ın çeviri kalitesini değerlendirme kriterleriyle uyumludur. Ayrıca bu çalışma, YZ dublajını kültürel manipülasyon üzerine daha geniş söylemler içinde konumlandırarak ve YZ aracılı kültürel aktarım süreçlerinde eleştirel çerçevelere ve insan müdahalesine duyulan ihtiyacı vurgulayarak, görsel-işitsel çeviri ve dijital medya etiği alanlarında giderek artan tartışmalara katkıda bulunmaktadır.

Anahtar Kelimeler

• Görsel-işitsel çeviri
• Dublaj çevirisi
• Yapay zekâ destekli dublaj
• Çeviri Kalitesi Değerlendirme Modeli
• YouTube.

Global Voices, Local Losses: The Quality Assessment of AI-generated Dubbing on the YouTube channel ‘Vlad’

Öz

AI-generated dubbing on YouTube has enabled fast, scalable multilingual content distribution, yet raises serious concerns about cultural manipulation, ideological reframing, and narrative control. With an emphasis on changes in tone, pragmatics, comedy, and cultural references, this study explores how AI-generated dubbing on YouTube shapes meaning. AI-generated dubbing often prioritizes just lip-sync accuracy and serving multiple language options to viewers over cultural integrity, leading to errors in meaning while supporting commercial goals, such as reaching more followers and encouraging them to subscribe to the channel. In line with this fact, the current study aims to examine how AI-generated dubbing affects cultural representation by conducting a qualitative review of data derived from purposive sampling of Turkish dubbing translations from the children’s content channel ‘Vlad’. The data derived from the study is classified according to recurring patterns under Larson’s Meaning-based Translation Quality Assessment (TQA) model. Meaning, purposefully selected samples are classified in the frame of five error types: (1) semantic inaccuracies, (2) pragmatic mismatches, (3) omissions and additions, (4) audiovisual misalignment, and (5) speech synthesis-related problems, which align with Larson’s criteria for evaluating translation quality, including accuracy, naturalness, and clarity. This study contributes to the growing conversations in audiovisual translation and digital media ethics by situating AI dubbing within broader discourses on cultural manipulation and emphasizing the need for critical frameworks and human intervention in AI-mediated cultural transfer processes.

Anahtar Kelimeler

• Audiovisual translation
• Dubbing translation
• AI-generated dubbing
• Translation Quality Assessment Model
• YouTube.

Kaynakça

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