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

Training LDCRF model on unsegmented sequences using Connectionist Temporal Classification