Emphasis engine
Dynamic captions that pop the right word.
Captions where one word per line is picked out and emphasised as you say it — and the interesting question is how the tool decides which word.
Dynamic captions are subtitles in which one word per line is automatically emphasised — recoloured, scaled, boxed or underlined as it is spoken — rather than the whole line appearing in one uniform style. LumaCaption picks that word with a transparent scoring rule you can override per line, and 119 of its 294 presets carry a keyword accent.
Every tool in this category advertises dynamic captions and almost none of them will tell you how the emphasis is chosen. It matters, because the entire value of the feature rests on it: a caption that emphasises the wrong word is worse than one that emphasises nothing, since it actively directs attention away from the point of the sentence.
The failure is easy to spot once you know to look. Tools that pick the longest word in the line will confidently highlight "actually" over "money". Tools that pick the first or last word emphasise whatever happened to land there. Tools that pick at random look intelligent for about four lines. And any tool tuned only on English will pick a Hindi filler word out of a Hinglish sentence, because it has no idea "matlab" and "yaar" carry no meaning.
So rather than describe ours as AI-powered and leave it there, the scoring rules are published below. You can check them against your own captions, and where the pick is wrong you can override it in one tap.
One word per line, not a light show
Exactly one word per cue takes the accent. Emphasising three words in a line emphasises nothing — the eye has no single place to land, which is the most common way this effect is over-applied.
Four kinds of emphasis, not just colour
Across the catalogue: 87 presets scale the word, 84 recolour it, 22 put it in a filled box, and 6 underline it. Some combine them. Colour alone fails over footage that happens to match the accent.
Hinglish-aware, not English-tuned
The weak-word list includes matlab, yaar, bhai, accha, haan, bas, chalo, dekho and suno alongside the English hedges — so a code-mixed sentence gets its emphasis on a content word rather than a discourse marker.
Override it in one tap
Star any word and it takes the accent for that line, permanently. The automatic pick is a starting point that is right most of the time, not a decision you have to live with.
The actual scoring rule
Every word in a cue is scored, and the highest score takes the accent. The scoring is deliberately boring and inspectable rather than learned, because a rule you can read is a rule you can predict — and predictability is what makes the feature usable at scale.
Length is a tiebreak rather than a driver, and this was a real correction: an early version weighted characters flat, which let "actually" beat "money" on nothing but span. Length now contributes at a diminishing rate and stops mattering past about eleven characters.
- Curated power word: +12
- A hand-maintained list of words that carry weight in spoken short-form — the ones a person would lean on when saying the sentence out loud.
- Contains a number: +9
- Numbers are almost always the point of the sentence they appear in. This is the single strongest signal after the power-word list.
- Money or percentage symbol: +6
- Stacks with the number bonus, so "₹50,000" scores far above a long ordinary noun.
- Ends in ! or ?: +7
- Shouted or asked. Bare punctuation tokens are excluded, or a typed script with a lone "!" would see it picked as the hero.
- ALL-CAPS token: +5
- Two or more capitals means it was shouted in the source text.
- Mid-line capitalised word: +4
- Reads as a proper noun — a name or a place, which is usually worth pointing at.
- Stopword: −12
- Connectives and articles are never the answer. The penalty is large enough that no length bonus can rescue them.
- Hedge or filler: −6
- The weak list — "basically", "stuff", "thing", and the Hinglish discourse markers. They lose to any real content word but can still win an otherwise empty line.
- Position lean: up to +1.5
- A slight bias toward the end of the line, because punchlines land late. Small enough to break ties without overriding meaning.
Where automatic emphasis goes wrong, and what to do
The rule is right most of the time and wrong in predictable places. Knowing which places saves you scanning every line.
It struggles when a cue contains two equally good candidates — a number and a proper noun in the same breath — because only one can win and the rule has no way to know which one your sentence was actually about. It also struggles on lines that are entirely functional, where the honest answer is that no word deserves emphasis; there the pick is arbitrary because the input is.
Both are one tap to fix. Star the word you want and it takes the accent for that line. If a whole passage should be calm, switch it to a preset with no keyword accent — 175 of the 294 have none.
How emphasis is carried across the catalogue
| Emphasis kind | Presets | What it does |
|---|---|---|
| Scale | 87 | The active word grows slightly as it is spoken — typically 4–6%. |
| Highlight | 84 | The word takes an accent colour distinct from the body text. |
| Box | 22 | A filled pill or bar sits behind the word and travels between words. |
| Underline | 6 | A rule is drawn under the active word. |
Counted from the preset catalogue on 26 August 2026; presets can combine kinds, so these do not sum to the 165 presets that carry any emphasis at all. 119 presets carry a keyword accent specifically.
How it works
Add your clip
Drop in the video. Every word is transcribed and timed individually, which is what makes per-word emphasis possible at all.
Pick a dynamic style
Any of the 119 presets with a keyword accent. The emphasis is chosen for every line the moment you select one.
Override where it matters
Read the hook line and the payoff line. Star a different word if the pick is wrong; the rest of the video is usually fine as-is.
Common questions
What are dynamic captions?
Subtitles where one word per line is automatically emphasised — recoloured, scaled, boxed or underlined at the moment it is spoken — instead of the whole line appearing in one uniform style. The effect keeps a viewer’s eye moving with the voice, which is why it holds attention better than static subtitles on short-form video.
How does the tool decide which word to emphasise?
Every word in a line is scored and the highest score wins. Numbers, money amounts, shouted words, proper nouns and a curated list of high-weight words score up; stopwords and hedges score sharply down; word length is only a tiebreak. The full scoring table is published above, and you can override the pick on any line by starring the word you want.
Are dynamic captions better than normal subtitles?
For short-form video watched on a phone, generally yes — they keep the eye tracking the voice rather than reading ahead and disengaging. For long-form, dense or instructional content they are usually worse, because emphasis applied to every line stops meaning anything and the constant movement is tiring. Use them where delivery carries the video, not where information does.
Can I turn the emphasis off?
Yes. 175 of the 294 presets carry no keyword accent at all, so switching to one of those gives you clean, uniform captions. You can also keep a dynamic style and override the emphasised word line by line.
Do dynamic captions work in Hindi and Hinglish?
Yes, and the scoring is specifically built for it. Devanagari characters are kept when words are normalised for scoring — an early version stripped them, which left every Hindi word tied and the pick stuck on the first word of every line. The weak-word list also includes Hinglish discourse markers like matlab, yaar and bas, so those are never chosen as the standout.
