Efficient combination of likelihood recycling and batch calculation based on conditional fast processing and acoustic back-off

This paper proposes an efficient combination of state likelihood recycling and batch state likelihood calculation for accelerating acoustic likelihood calculation in an HMM-based speech recognizer. Recycling and batch calculation are each based on different technical approaches, i.e. the former is a purely algorithmic technique while the latter fully exploits PC architecture, and their good acceleration performances are reported in the literatures, respectively. To accelerate the recognition process further by combining them efficiently, we introduce conditional fast processing and acoustic back-off strategies. Our combination algorithm employs the conditional fast processing strategy that is conditioned by two criteria. The first potential activity criterion is used to control not only the recycling of state likelihoods at the current frame but also the precalculation of state likelihoods for several succeeding frames. The second reliability criterion and acoustic back-off are used to control the choice of recycled or batch calculated state likelihoods when they are contradictory in the combination and to prevent word accuracies from degrading. Large vocabulary spontaneous speech recognition experiments using four PCs with different specifications showed that, despite the PC specification dependence, the combined acceleration technique further reduced the total recognition time on all of the PCs.