AI and Machine Learning Aim to Fix Entertainment's Global Translation Challenges
Recent translation controversies in hit shows like "Squid Game" highlight the flaws in traditional media localization. Artificial intelligence and machine learning now emerge as potential solutions to speed up and improve this complex process.
Recent controversies surrounding mistranslations in hit shows like "Squid Game" highlight the significant challenges of distributing media across global markets. With thousands of movies and episodes released annually across hundreds of streaming platforms, the industry struggles to accurately translate content for billions of viewers who speak thousands of different languages.
Traditional localization is a slow, human-centered process that involves translating dialogue scripts and hiring voice actors to match lip movements for audio dubs. This lengthy workflow often takes weeks per language, and compromises made during the initial translation phase are frequently compounded when generating the final subtitles, leading to lost cultural context and altered storylines.
To handle the exponential growth of streaming content and the continuous demand for fresh media, the industry now turns to artificial intelligence and machine learning as anticipated solutions. While these advanced technologies show great promise for speeding up production and increasing translation accuracy, they have not yet reached the point of completely replacing the nuanced cultural understanding required for high-quality localization.