Linear dynamic segmental HMMs: variability representation and training procedure

This paper describes investigations into the use of linear dynamic segmental hidden Markov models (SHMMs) for modelling speech feature-vector trajectories and their associated variability. These models use linear trajectories to describe how features change over time, and distinguish between extra-segmental variability of different trajectories and intra-segmental variability of individual observations around any one trajectory. Analyses of mel cepstrum features have indicated that a linear trajectory is a reasonable approximation when using models with three states per phone. Good recognition performance has been demonstrated with linear SHMMs. This performance is, however, dependent on the model initialisation and training strategy, and on representing the distributions accurately according to the model assumptions.