Reformulating the HMM as a trajectory model by imposing explicit relationships between static and dynamic feature vector sequences

In the present paper, a trajectory model, derived from the hidden Markov model (HMM) by imposing explicit relationships between static and dynamic feature vector sequences, is developed and evaluated. The derived model, named trajectory HMM, can alleviate some limitations of the standard HMM, which are i) piece-wise constant statistics within a state and ii) conditional independence assumption of state output probabilities, without increasing the number of model parameters. In the present paper, a Viterbi-type training algorithm based on a maximum likelihood criterion is also derived. The trajectory HMM was evaluated both in speech recognition and synthesis experiments. In the speaker-dependent continuous speech recognition experiment, the trajectory HMM achieved error reduction over the standard HMM. The subjective listening test results show that introduction of the trajectory HMM can improve the quality of HMM-based speech synthesis system which we have proposed.