Segmental hidden Markov models (HMM's) are extended versions of conven
tional HMM's in which states are associated with sequences of observat
ion vectors rather than individual vectors, By treating a segment as a
homogeneous unit, dependencies between vectors within a segment can b
e modeled explicitly. This letter describes a segmental HMM in which a
segment is modeled as a noisy function of a linear trajectory. The ba
sic theory of the model is presented, together with formulae for model
parameter optimization.