  Many machine learning problems such as speech recognition, gesture
recognition, and handwriting recognition are concerned with simultaneous
segmentation and labeling of sequence data. Latent-dynamic conditional random
field (LDCRF) is a well-known discriminative method that has been successfully
used for this task. However, LDCRF can only be trained with pre-segmented data
sequences in which the label of each frame is available apriori. In the realm
of neural networks, the invention of connectionist temporal classification
(CTC) made it possible to train recurrent neural networks on unsegmented
sequences with great success. In this paper, we use CTC to train an LDCRF model
on unsegmented sequences. Experimental results on two gesture recognition tasks
show that the proposed method outperforms LDCRFs, hidden Markov models, and
conditional random fields.
