A two-layer lexical tree based beam search in continuous Chinese speech recognition

In this paper, an approach to continuous speech recognition based on a two-layer lexical tree is proposed. The search network is maintained by the two-layer lexical tree, in which the first layer reflects the word net and the phone net while the second layer the dynamic programming (DP). Because the acoustic information is tied in the second layer, the memory cost is so small that it has the ability to process some complicated applications, such as the use of cross-word context-dependent (CD) triphone models, the Chinese fuzzy syllable mapping and the pronunciation modeling. The search algorithm based on the two-layer lexical tree is also proposed, which is derived from the token-passing algorithm. Finally, an implementation of the two-layer lexical tree using the crossword context-dependent triphone models is presented, and the experimental results show that the highly efficient decoding can be achieved without too much memory cost.