Concurrent Optimization of Context Clustering and GMM for Offline Handwritten Word Recognition Using HMM

Context-dependent HMMs are commonly used in speech recognition. Parameter sharing needed for this model can be realized by two methods: context clustering or tied-mixture. In speech recognition, the former is reported to be more precise. However, there is some difficulty in applying context clustering to handwritten word recognition, since the distribution of each character is typically a mixture of different distributions, such as block-printed, cursive, etc. For this reason, successful results reported so far are limited to the tied-mixture approach. To deal with this problem, we propose a novel parameter tying method "Partial Tied-Mixture", where the Gaussian Mixture Model (GMM) consists of a portion of all Gaussians. Furthermore, we derive a method to concurrently optimize context clustering and GMM. Experiments on the CEDAR database show that the proposed method outperforms tied-mixture both in terms of precision and computational cost.