TECHNICAL
are linguistically coherent and visually balanced, without requiring manual formatting. For example, NLP-based systems can ensure that dependent words — such as negations, currency symbols, or compound names — are not split across lines. They can also maintain visual gaps between captions to signal speaker changes, improving viewer comprehension. (See Figure 1) Caption segmentation becomes even more complex in multilingual environments. Languages like German, Japanese or Arabic present unique challenges — compound words, lack of spacing or different punctuation norms. AI-powered NLP systems overcome these barriers by analysing sentence structure and meaning, identifying optimal break points without requiring fluency in each language. This allows captions to maintain linguistic integrity and readability across diverse audiences, eliminating the need for costly language specialists and reducing segmentation errors.
verifying that caption files meet standards for frame rate, timecodes, character sets and indexing. This prevents decoding errors and ensures compatibility across platforms. Additionally, AI-driven spell checkers can detect and correct typographical errors and homophones (e.g., “their” vs. “they’re”) using context- aware language models. These refinements enhance professionalism and viewer trust, especially in fast-paced or technical content where manual review may miss subtle mistakes. AI systems can also verify that the correct language is paired with the correct audio track using Language Identification (LID), helping prevent distribution errors in multilingual environments. This ensures that viewers receive accurate subtitles in their intended language, improving accessibility.
2. Computer Vision (CV)
Caption placement is critical to viewer experience. Poorly positioned captions can obscure faces, scores or embedded text. CV models detect visual elements in each frame and flag overlap issues. These models can also identify lip movements and speaker locations, allowing captions to be placed near the speaking character. This not only improves readability but also enhances synchronisation. CV-driven placement algorithms ensure captions are optimally positioned without manual inspection. Additionally, CV-based systems can detect scene changes, a process that plays a crucial role in refining caption timing. By analysing pixel values, edge transitions or motion vectors between frames, automated systems can identify visual transitions — such as shifts in location, time or character focus — and adjust caption in/out points accordingly to ensure captions appear and disappear in sync. This helps prevent captions from lingering across scene boundaries, which can confuse viewers and disrupt narrative flow. Traditional workflows often overlook these subtleties, but automated solutions ensure captions align with shot boundaries, enhancing coherence and viewer comprehension.
5. Multimodal AI
The most transformative advances come from multimodal AI systems that integrate audio, video and text. These models understand context across modalities, enabling more nuanced QC. For example, multimodal AI can detect non-verbal events like laughter, applause or gunshots and insert appropriate audio descriptions (e.g., “[Car honks loudly]”). It can also interpret facial expressions to adjust tone in captions (e.g., replacing “Okay.” with “Okay…” [hesitant tone]). By combining ASR with lip-reading and scene-change detection, multimodal systems improve synchronisation and speaker identification, even in overlapping
4. Natural Language Processing (NLP)
Effective captions are not just accurate — they must be well-segmented and readable. NLP tools analyse sentence structure using dependency trees, which map syntactic relationships between words. This allows captions to be broken at natural clause boundaries, preserving the flow of dialogue. Dynamic programming algorithms optimise segmentation by balancing character limits, line breaks and proportional line lengths. This ensures captions
3. Machine Translation (MT)
Global distribution often requires subtitles in 30+ languages. Traditional MT systems struggle with context, leading to literal translations that miss the tone or intent of the original dialogue. AI-powered MT models, especially those integrated with LLMs, use contextual embeddings to produce fluent, culturally appropriate translations. New evaluation metrics assess translation quality beyond grammar, considering fluency and cultural relevance. Specialised MT models trained on subtitle datasets further improve dialogue-style translations, making multilingual QC more reliable and scalable.
Beyond timing and translation, AI also ensures metadata conformance —
Figure 1: Dependency tree illustrating the syntactic segmentation of the sentence “this technique can be considered as the simplest way to perform segmentation.”
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SEPTEMBER 2025 Volume 47 No.3
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