Linear trajectory segmental HMMs

Much of the progress in automatic speech recognition is attributable to the use of hidden Markov models (HMMs) to characterize acoustic speech patterns. Despite their success, HMMs make little use of knowledge about the speech signal, and variation that may be explicable in terms of the physical properties of the human speech production system is treated as random. There is, therefore, a need to develop a speech modeling paradigm which reflects human speech processes more closely. Segmental hidden Markov models (HMMs) are extended versions of conventional HMMs in which states are associated with sequences of observation vectors rather than individual vectors. By treating a segment as a homogeneous unit, dependencies between vectors within a segment can be modeled explicitly. This letter describes a segmental HMM in which a segment is modeled as a noisy function of a linear trajectory. The basic theory of the model is presented, together with formulae for model parameter optimization.