Lip-sync that holds up: the metrics we obsess over
AI & Technology 6 min read Apr 30, 2026

Lip-sync that holds up: the metrics we obsess over

Phoneme alignment, viseme matching, and a handful of perceptual thresholds are what separate a convincing dub from an uncanny one. Here's what we actually measure.

Lip-sync is one of those things audiences only notice when it's wrong, and then they can't un-notice it. Getting it right is less about a single clever model and more about respecting a few stubborn numbers.

Phoneme alignment comes first

Before anything looks right, the sounds have to land in the right place in time. We align the dubbed phonemes to the original mouth movements and score the drift in milliseconds.

Alignment error before vs. after tuning
Raw TTS120ms
Aligned45ms
Target<30ms
Average offset between spoken phoneme and on-screen mouth. Under ~30ms reads as "in sync".

Visemes: matching the shapes

Timing isn't enough, the mouth shapes have to plausibly match. Bilabials like b, m and p close the lips; if the dub opens the mouth there, viewers feel the wrongness even if they can't name it.

Timing only

  • Words land on time
  • But shapes can conflict
  • Closed-lip sounds leak
  • Subtle "off" feeling

Timing + visemes

  • Words land on time
  • Lip shapes plausibly match
  • Bilabials close correctly
  • Reads as natural

The perceptual threshold

Ultimately the only judge is a human eye. We panel-test cuts and track the point where viewers stop reporting a "dubbed" feeling, our real pass mark.

91%read as natural
Share of viewers who rated a tuned cut as "not distractingly dubbed".

The pipeline that holds it together

01DetectMouth + phoneme track
02AlignTime to <30ms
03MatchViseme shapes
04PanelHuman perceptual QC

None of these numbers is glamorous. But respect all of them at once and you get the thing audiences never comment on, a dub that simply disappears into the story.

#Lip-sync#Metrics#Dubbing
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