Incorporating syntax into the level-building algorithm on a tree-structured parallel computer

The authors investigate the parallel computation of large-vocabulary speech recognition on a tree-structured parallel processor. Having seriously established results on parallel level-building with continuously variable hidden Markov models they extend this work to the case in which the spoken sentences are constrained by a finite state grammar. The two key ideas are: (1) a pipelined sorting function on the processor array that efficiently transmits a sorted list of the best scores from all processors to the host; and (2) a level-based pruning technique in which paths through the dynamic programming network are pruned only at the ends of words. These ideas are evaluated on the BT-100 processor, a binary-tree parallel processor. A performance model is presented that estimates execution time as a function of algorithm parameters. Real-time speech recognition has been achieved for a data-entry task with a 70-word vocabulary and average branching factor of 23, and for an airline reservation task with a vocabulary of 132 words. The performance model predicts real-time execution of the 991-word DARPA Resource Management Task on a 127-processor machine