Trajectory modeling based on HMMs with the explicit relationship between static and dynamic features

This paper shows that the HMM whose state output vector includes static and dynamic feature parameters can be reformulated as a trajectory model by imposing the explicit relationship between the static and dynamic features. The derived model, named trajectory HMM, can alleviate the limitations of HMMs: i) constant statistics within an HMM state and ii) independence assumption of state output probabilities. We also derive a Viterbi-type training algorithm for the trajectory HMM. A preliminary speech recognition experiment based on N-best rescoring demonstrates that the training algorithm can improve the recognition performance significantly even though the trajectory HMM has the same parameterization as the standard HMM.