A unified way in incorporating segmental feature and segmental model into HMM

There are two major approaches to speech recognition: frame-based and segment-based approach. Frame-based approach, e.g. HMM, assumes statistical independence and identical distribution of observation in each state. In addition it incorporates weak duration constrains. Segmentbased approach is computational expensive and rough modelling easilly occurs if not much 'templates' are stored. This paper presents a new framework to incorporate segmental feature and segmental model in a d i e d way into frame-based HMM to exploit the advantage of both methods. In the modified Viterbi algorithm, frame-based information prunes out the most probable path at each segment level to which segmental model can be applied with dramatically reduced computational load: at the same time, segmental score refines the score obtained by framebased model at each level. In this way. the best path found in the end of Viterbi algorithm is optimal in both sense