Embedded speech processing systems require stringent memory allocation and computing resources. To minimize such resources, a simple, flexible hidden Markov model (HMM) evaluation technique is presented which employs a state-space formulation in conjunction with a simplified likelihood measure. The method offers several advantages including the ability to reduce redundant computation and memory allocation across models, and a flexible structure that can exploit known results concerning statespace systems. Although performance is insignificantly effected in preliminary experiments, these benefits are achieved at the cost of a weaker coupling between the two stochastic processes that define the HMM. We augment the method with a Markov chain model of the observations to compensate for the weaker state coupling. Preliminary experiments are used to analyze recognition performance and as a basis for discussion.
['Yong-beom Lee', 'R John Deller']
Heuristic structural modifications to the HMM for efficient resource utilization