As someone who’s been in the film transcription and captioning business for over 15 years, I’ve witnessed firsthand the rapid evolution of captioning technology. AI has made impressive strides in the captioning world, revolutionizing workflows and creating new possibilities. However, while there is a space for AI captioning to exist, premium content like television shows and films, human captioning is still the de facto way of getting the results that the distributors and streaming platforms expect.
1. Speaker Identification Failures
AI captioning systems struggle with both identifying speakers and distinguishing between different speakers, creating confusion that makes content difficult to follow.
During a TV show discussion, featuring four female speakers with similar voices, the AI captioning assigned all dialogue to a single speaker, creating a confusing transcript where opposing viewpoints appeared to come from the same person.
Professional captioners handle speaker identification expertly by:
- Including speaker IDs only when the speaker is off-screen
- Tracking when characters are formally introduced
- Distinguishing between similar voices even during rapid exchanges
- Using appropriate descriptors for unidentified speakers and preserving storytelling elements through careful speaker labeling
2. No Caption Positioning
AI captioning cannot intelligently position captions to avoid covering on-screen text, graphics, or lower thirds.
In a travel documentary, captions consistently obscured location names displayed at the bottom of the screen. When lower-third identifications appeared to introduce speakers, the AI captions covered these completely, leaving deaf viewers unable to identify the experts.
Professional captioners strategically position captions by:
- Adjusting caption placement to avoid covering on-screen text or graphics
- Temporarily moving captions to the top of the screen when lower thirds appear
- Reducing caption lines when necessary to prevent obstruction of visual elements
3. Sound Effect Errors
Closed captioning requires the mention of sound effects, and AI transcription either can’t or has a hard time identifying the type of sound effect since it does not have any visual cues.
During a horror film I captioned, an AI system simply noted [noise] for several critical atmospheric sounds—missing the difference between [floorboards creaking], [distant scream], and [door slowly opening], which were crucial to understanding the building tension for deaf and hard-of-hearing viewers.
Professional captioners provide precise sound effect descriptions by:
- Distinguishing between similar sounds
- Understanding which sounds advance the narrative
- Providing appropriate detail without overwhelming the viewer
4. Inability to Format Natural Line Breaks
Captioning requires proper line and caption breaks which makes it easy for the viewer to read the captions. AI captions are not able to give natural line and caption breaks.
AI Captioning (Poor):
Rising temperatures have led to
severe droughts in regions that
once had abundant rainfall throughout
the year.
Human Captioning (Proper):
Rising temperatures have led
to severe droughts in regions
that once had abundant rainfall
throughout the year.
The AI version broke phrases unnaturally and created an unbalanced caption with some lines much shorter than others. In another instance, the AI created single-word captions for emphasis words that flashed too quickly for comfortable reading.
Human captioners create readable captions by:
- Breaking lines at natural linguistic boundaries
- Maintaining proper reading speed (about 15-17 characters per second)
- Creating caption groups that represent complete thoughts
- Ensuring no caption exceeds 2-3 lines for readability
5. Poor Performance with Accents
I recently worked on a documentary featuring speakers from five different countries, and the AI transcript was nearly unusable as it couldn’t transcribe the different English accents properly.
The documentary featured experts from India, Nigeria, Scotland, Japan, and Brazil—each speaking English with their native accents. The AI system struggled with every non-native speaker, capturing less than 90% of the content accurately. The Nigerian historian’s discussion of “colonial architecture” became “calling all ark texture,” while the Indian archaeologist’s explanation of “excavation techniques” was transcribed as “extra patient techniques.”
Human captioners excel at understanding diverse speech patterns because we:
- Process context clues beyond individual words
- Recognize regional pronunciation differences
- Apply cultural knowledge to ambiguous phrases
6. Audio Sync Issues
- Making real-time adjustments for pace changes
- Creating scene-appropriate timing for emotional impact
- Ensuring proper display duration for each caption
- Manually verifying sync points throughout long programs
7. Struggles with Background Noise
The background noise or sound effects that are present in films or TV shows can overlap speech sometimes, and AI transcription is not able to detect it, leading to incomplete sentences.
When captioning a scene from an action film where crucial dialogue occurred during a car chase, the AI transcription completely missed the protagonist’s whispered “It’s a trap” because of engine noises.
Human captioners expertly handle noisy environments by:
- Filtering out irrelevant background noise mentally
- Reconstructing partially obscured speech based on context
8. Inaccuracies from Overlapping Speech
Human captioners expertly handle overlapping speech by:
- Distinguishing between multiple simultaneous voices
- Prioritizing the most important information when voices overlap
- Using special formatting techniques to indicate interrupted speech
- Capturing cross-talk in ways that preserve meaning and speaker intent
9. Improper Speaker IDs
Speaker IDs are only required in captioning when there is a change in speaker and the speaker can’t be seen on-screen. Since AI does not have any visual cues, this nuance is lost.
During a documentary interview featuring cutaway shots, the AI captions continued to identify the speaker even when they were clearly visible on screen—creating redundant and distracting captions.
Professional captioners follow precise FCC rules by:
- Including speaker IDs only when the speaker is off-screen
- Removing IDs when speakers become visible
10. Improper Sound Effects Contextualization
Without visual context, AI systems often include unnecessary sound effect descriptions or miss critical ones, reducing accessibility.
In a nature documentary, an AI system captioned [birds chirping] for background ambience that wasn’t relevant to the narrative, but missed a critical [thunder rumbling] that visually prompted the presenter to look upward and discuss an approaching storm. This disconnection between captions and visual cues left deaf viewers missing key context.
Human captioners excel at contextualizing sound by:
- Only captioning sounds not obvious from visuals
- Properly describing the intensity and nature of sounds
- Understanding when sound effects drive the narrative
While AI captioning technology continues to improve, these ten critical limitations demonstrate why human expertise remains essential for professional captioning. For short social media clips or internal rough cuts, AI might suffice. But for films, TV shows, documentaries, educational content, and any professional media requiring accessibility compliance, human captioners deliver the accuracy, context awareness, and quality that viewers deserve.
At Caption Easy, we leverage our decade of experience and human expertise to ensure your content receives the highest quality closed captioning possible. With over 1,000 successfully captioned films, we understand the nuances that technology simply can’t grasp. Contact us today to learn how our professional captioning services can make your content truly accessible to all viewers.