Training LDCRF model on unsegmented sequences using Connectionist Temporal Classification

TL;DR

This paper introduces a method to train LDCRF models directly on unsegmented sequence data by integrating Connectionist Temporal Classification, enabling improved gesture recognition performance without prior segmentation.

Contribution

It presents a novel approach combining LDCRF with CTC to handle unsegmented data, which was not possible with traditional LDCRF training methods.

Findings

Outperforms traditional LDCRF, HMM, and CRF models on gesture recognition tasks.

Enables training of LDCRF on unsegmented sequences.

Demonstrates significant accuracy improvements in experimental results.

Abstract

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.