Do VSR Models Generalize Beyond LRS3?
Yasser Abdelaziz Dahou Djilali Sanath Narayan Eustache Le Bihan Haithem Boussaid Ebtessam Almazrouei Merouane Debbah
The Lip Reading Sentences-3 (LRS3) benchmark has primarily been the focus of
intense research in visual speech recognition (VSR) during the last few years.
As a result, there is an increased risk of overfitting to its excessively used
test set, which is only one hour duration. To alleviate this issue, we build a
new VSR test set named WildVSR, by closely following the LRS3 dataset creation
processes. We then evaluate and analyse the extent to which the current VSR
models generalize to the new test data. We evaluate a broad range of publicly
available VSR models and find significant drops in performance on our test set,
compared to their corresponding LRS3 results. Our results suggest that the
increase in word error rates is caused by the models inability to generalize to
slightly harder and in the wild lip sequences than those found in the LRS3 test
set. Our new test benchmark is made public in order to enable future research
towards more robust VSR models.