A study on Hidden Structural Model and its application to labeling sequences

This paper proposes Hidden Structure Model (HSM) for statistical modeling of sequence data. The HSM generalizes our previous proposal on structural representation by introducing hidden states and probabilistic models. Compared with the previous structural representation, HSM not only can solve the problem of misalignment of events, but also can conduct structure-based decoding, which allows us to apply HSM to general speech recognition tasks. Different from HMM, HSM accounts for the probability of both locally absolute and globally contrastive features. This paper focuses on the fundamental formulation and theories of HSM. We also develop methods for the problems of state inference, probability calculation and parameter estimation of HSM. Especially, we show that the state inference of HSM can be reduced to a quadratic programming problem. We carry out two experiments to examine the performance of HSM on labeling sequences. The first experiment tests HSM by using artificially transformed sequences, and the second experiment is based on a Japanese corpus of connected vowel utterances. The experimental results demonstrate the effectiveness of HSM.