Aggregation Operators and Hypothesis Space Reductions in Speech Recognition

In this paper we deal with the heuristic exploration of general hypothesis spaces arising both in the HMM and segment-based approaches of speech recognition. The generated hypothesis space is a tree where we assign costs to its nodes. The tree and the costs are both generated in a top-down way where we have node extension rules and aggregation operators for the cost calculation. We introduce a special set of mean aggregation operators suitable for speech recognition tasks. Then we discuss the efficiency of some heuristic search methods like the Viterbi beam search, multi-stack decoding algorithm, and some improvements using these aggregation operators. The tests showed that this technique could significantly speed up the recognition process. The run-times we obtained were 2 times faster than the basic multi-stack decoding method, and 4 times faster than the Viterbi beam search method.