A fast tree-trellis search for finding the N-best sentence hypotheses in continuous speech recognition

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Abstract
In this paper a tree—trellis-based fast search for finding the N-best sentence hypotheses in continuous speech recognition is proposed. The search consists of two parts: a forward, time-synchronous, trellis search and a backward, time asynchronous, tree search. In the first module the well-known Viterbi algorithm is used for preparing a map of all partial paths and the corresponding scores, time synchronously. In the second module a tree search is used to grow partial paths backward and time asynchronously. Each partial path in the backward tree search is ranked ordered in a stack by the corresponding full path score, which is computed by adding the partial path score with the best possible score of the remaining path obtained from the trellis path map. In each path growing cycle, the current best partial path, which is at the top of the stack, is extended by one arc (word). The new tree-trellis search is different from the traditional time synchronous Viterbi search in its ability for finding not just the best but the N-best paths of different word content. The new search is also different from the A* algorithm, or the stack algorithm, in its capability for providing an exact, full path score estimate of any given partial (i.e., incomplete) path before its completion. When compared with the best candidate Viterbi search, the search complexities for finding the N-best strings are rather low, i.e., only a fractional increase in computation is needed. Also, only a marginal increase of memory space is required for implementing the tree-trellis algorithm. This new algorithm provides a natural interface between a continuous speech recognizer and its higher level processing modules of a speech understanding system.

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