In the context of Cued Speech (CS) recognition, the recognition
of lips and hand movements is a key task. As we know, a good
temporal segmentation is necessary for the supervised recog-
nition system. However, lips and hand streams cannot share
the same temporal segmentation since they are not synchro-
nized. In this work, we propose a hand preceding model to
predict temporal segmentations of hand movements automati-
cally by exploring the relationship between hand preceding time
and the vowel positions in sentences. To evaluate the perfor-
mance of the proposed method, we apply the hand preceding
model to a multi-speakers database. Hand positions recognition
is realized with the multi-Gaussian and Long-Short Term Mem-
ory (LSTM). The results show that using the predicted tempo-
ral segmentation significantly improves the recognition perfor-
mance compared with that using the audio based segmentation.
To the best of our knowledge, this is the first automatic method
to predict the temporal segmentation for hand movements only
from the audio based segmentation in CS.