HMM with global path constraint in Viterbi decoding for isolated word recognition

Hidden Markov models (HMMs) with explicit state duration density (HMM/SD) can represent the time-varying characteristics of speech signals more accurately. However, such an advantage is reduced in relatively smooth state duration densities or long bounded duration. To solve this problem, the authors propose HMMs with global path constraint (HMM/GPC) where the transition between states occur only within prescribed time slots. HMM/GPC explicitly limits state durations and accurately describes the temporal structure of speech simply and efficiently. HMMs formed by combining HMM/GPC with HMM/SD are also presented (HMM/SD+GPC) and performances are compared. HMM/GPC can be implemented with slight modifications to the conventional Viterbi algorithm. HMM/GPC and HMM/SD+GPC not only show superior performance than the conventional HMM and HMM/SD but also require much less computation