Improving the Multi-stack Decoding Algorithm in a Segment-Based Speech Recognizer

During automatic speech recognition selecting the best hypothesis over a combinatorially huge hypothesis space is a very hard task, so selecting fast and efficient heuristics is a reasonable strategy. In this paper a general purpose heuristic, the multi-stack decoding method, was refined in several ways. For comparison, these improved methods were tested along with the well-known Viterbi beam search algorithm on a Hungarian number recognition task where the aim was to minimize the scanned hypothesis elements during the search process. The test showed that our method runs 6 times faster than the basic multi-stack decoding method, and 9 times faster than the Viterbi beam search method.