Subtitles generated by AI alone

Key Question of the Month:

Based on our experience, subtitles generated by AI alone are not acceptable for our quality standards. While the output may be understandable, it often diminishes the viewing experience due to dull, inconsistent, or awkward translations. Have you seen any improvements in AI subtitling performance recently?

ChatGPT’s Insight:

Your observation is very common in the industry and matches what many professionals report: AI-generated subtitles are often understandable, but lack naturalness, timing, emotion, and cultural nuance—ultimately harming the viewer experience. They can be literal, awkward, or fail to capture humor, tone, and subtext. Even big providers like Netflix, Disney, and large LSPs rarely use fully automated subtitles without human post-editing.

Current (2024) State of AI in Subtitling

What’s Improved?

• Speech recognition (ASR): AI is now extremely good at transcribing audio (almost human-level for clear speech).

• Timecoding & segmentation: Automatic tools can segment and sync lines with high accuracy.

• Raw translation: Large models like GPT-4o, DeepL, and Google NMT have become much better at producing understandable translations, even handling slang and idioms better than before.

What’s Still Lacking?

• Naturalness & Engagement: AI still often produces subtitles that are dull, robotic, or too literal. It may miss cultural references, wordplay, and humor. Emotional tone and register are not reliably preserved.

• Brevity & Subtitle-Specific Style: Subtitling is an art—lines must fit time and space constraints, read naturally in 2 seconds, and sometimes adapt, not just translate. AI has improved but still struggles to optimize for “subtitle language.”

• Consistency: In longer videos or series, AI can be inconsistent with names, style, and terminology.

• Context Awareness: AI doesn’t always keep track of character relationships, running jokes, or evolving tone across an episode/film.

Recent Developments

• AI is improving and can be prompted for style (“Translate for subtitles, keep it brief and natural, adapt humor for Turkish audience”), which does help somewhat.

• New platforms are offering “subtitle-optimized MT engines,” but for most high-quality needs, post-editing by a professional subtitler is still essential.

Industry Practice (2024)

• For premium content (TV, streaming, cinema, educational):

MT is used for pre-translation, then a trained subtitler does a thorough post-edit to ensure viewing quality and audience engagement.

• For very low-budget or internal use:

Fully automated subtitles are sometimes accepted—but clients are warned about the limitations.

Should You Use AI Alone for Subtitling?

Not for content you care about or where audience experience matters.

But you can:

• Use AI for first drafts or less critical projects to save time/cost.

• Always include a professional subtitler for post-editing if quality is important.

Bottom line:

• AI performance in subtitling has improved in raw accuracy, but not enough for truly enjoyable, professional results without human post-editing.

• If client or audience experience matters, always include a subtitler

Our Take:

At ALS, we believe the combination of AI efficiency and professional subtitling expertise delivers the best results for our clients. 

We always recommend a human-in-the-loop approach for any content where quality and audience experience truly matter.