Statistical knowledge based frame synchronous search strategies in continuous speech recognition

In this paper, we propose a novel and efficient search algorithm for the Continuous Speech Recognition (CSR). The proposed algorithm is on the basis of the traditional Frame Synchronous Search (FSS) algorithm. It makes full use of some statistical knowledge, such as the Differential State Dwelling Distribution (DSDD), as one of the control factors for the state transition. It also incorporates some other rule-based knowledge, such as the pruning criterion based on the dynamic forward prediction and the lexical Word Search Tree (WST), as the search constraint. Experimental result shows that the statistical knowledge based search strategies can improve the performance of the CSR system significantly with an increase of the accuracy by 36.6% compared with the baseline FSS. Also, EasyTalk, the Chinese CSR system based on it, has achieved higher recognition accuracy and decoding efficiency.