The Lipreading Gap: Do VSR Models Perceive Visual Speech Like Human Lipreaders?
Visual speech recognition (VSR) models now surpass human lipreaders on benchmarks, but do such gains establish human-like visual speech perception? To explore this, we compare three VSR systems with human baselines on the MaFI word-level lipreading dataset using word, character, phoneme, and viseme-level metrics. Although models achieve higher overall accuracy, they succeed and fail on different words than humans. A text-only n-gram baseline given only a few initial phonemes rivals human lipreading. VSR word-level errors are consistently better explained by training word frequency than by the visual informativeness of words. Viseme accuracies, confusion matrices and human-model correlatio...
| Authors | Rishabh Jain, Naomi Harte |
|---|---|
| Primary category | cs.CV |
| Published | 5 June 2026 |
| Last updated at source | 8 June 2026 |
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