Large Language Models (LLMs) are transforming subtitle localisation for broadcasters and over-the-top (OTT) platforms by addressing long-standing issues with timing, context, and translation quality.
Writing in a recent blog post, Andy Chang, CTO, AsiaSat Media Technology (AMT), said, “While traditional neural machine translation (NMT) has facilitated subtitle localisation, it has inherent limitations.
“As NMT processes each segment independently, it cannot retain the full context of conversations. This results in translations that may feel clumsy or disjointed, particularly in fast-paced live environments where accuracy and timing are critical.”
In standard workflows, content is transcribed, segmented, translated, and reassembled, which can break narrative continuity and misalign subtitles with speech rhythm.
Live broadcasts intensify these issues, requiring manual intervention, higher costs, and risking audience disengagement.
LLMs, Chang noted, counter these limitations by processing entire transcripts with timestamps instead of independent fragments. This holistic approach enables coherent, natural translations that maintain contextual flow and align accurately with speaker timing.
In live content, LLMs dynamically refer to translation history, improving fluency and consistency in real time. For audiences, the result is smoother, synchronised subtitles that enhance comprehension and engagement.
For broadcasters, this yields multiple advantages, as Chang explained, “The implications for broadcasters and OTT platforms are significant. LLMs improve translation quality by producing human-like subtitles that are tightly synchronised with audio.
“They also make large-scale subtitling more affordable, as smaller models such as AWS Nova and Qwen-MT have shown that they can deliver excellent results at a lower computational cost.
“Equally important, LLM-based workflows are adaptable to both file-based operations, such as OTT library localisation, and live events, including sports and news. This versatility allows broadcasters to scale their localisation efforts without sacrificing quality or inflating operational budgets.”
However, challenges remain. LLMs may hallucinate (produce inaccuracies), and live use demands low latency and careful resource planning. Hybrid workflows combining LLMs with traditional NMT are emerging as practical solutions. Prospects include LLM-assisted dubbing, on-premises pipelines for security and cost savings, and multi-modal models incorporating audio or video cues for richer contextual understanding.
“For Asian broadcasters and OTT platforms, LLMs represent more than a technical upgrade. They address the long-standing timing and context issues of traditional NMT, enabling the delivery of high-quality, synchronised subtitles at scale.
“The outcome is a smoother viewing experience, reduced operational costs, and greater confidence in content readiness for global distribution,” Chang concluded.